Sample records for time series datastreams

  1. Probabilistic time-series

    E-Print Network [OSTI]

    Roweis, Sam

    SCIA 2003 Tutorial: Hidden Markov Models Sam Roweis, University of Toronto June 29, 2003 Probabilistic Generative Models for Time Series #15; Stochastic models for time-series: y 1 ; y 2 ; : : : ; y #15; Add noise to make the system stochastic: p(y t jy t 1 ;y t 2 ; : : : ;y t k ) #15; Markov models

  2. Regression quantiles for time series

    E-Print Network [OSTI]

    Cai, Zongwu

    2002-02-01T23:59:59.000Z

    ~see, e+g+, Ibragimov and Linnik, 1971, p+ 316!+ Namely, partition REGRESSION QUANTILES FOR TIME SERIES 187 $1, + + + , n% into 2qn 1 1 subsets with large block of size r 5 rn and small block of size s 5 sn+ Set q 5 qn 5 ? n rn 1 sn? , (A.7) where {x...! are the standard Lindeberg–Feller conditions for asymptotic normality of Qn,1 for the independent setup+ Let us first establish ~A+8!+ To this effect, we define the large-block size rn by rn 5 {~nhn!102} and the small-block size sn 5 {~nhn!1020log n}+ Then, as n r...

  3. Exact Primitives for Time Series Data Mining

    E-Print Network [OSTI]

    Mueen, Abdullah Al

    2012-01-01T23:59:59.000Z

    142 Sony AIBO Robot: Surfacetrajectories and ac- celerometer signals from SONY AIBOclasses of time series from the SONY AIBO accelerometer. (b)

  4. Statistical criteria for characterizing irradiance time series.

    SciTech Connect (OSTI)

    Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

    2010-10-01T23:59:59.000Z

    We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

  5. DCU Library User Guide -DataStream Advance 5.1 What is DataStream?

    E-Print Network [OSTI]

    Humphrys, Mark

    on Criteria Search, enter your search terms and click on Search. 5. Double-click on the DS Mnemonic you want on the designated DataStream PC in the Library's information commons. You'll be prompted to "Enter Password". Type is not broken!). 2. At Novell Login: "Workstation only" must be ticked. This automatically enters "datastream

  6. Turbulencelike Behavior of Seismic Time Series

    SciTech Connect (OSTI)

    Manshour, P.; Saberi, S. [Department of Physics, Sharif University of Technology, Tehran 11155-9161 (Iran, Islamic Republic of); Sahimi, Muhammad [Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-1211 (United States); Peinke, J. [Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); Pacheco, Amalio F. [Department of Theoretical Physics, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza (Spain); Rahimi Tabar, M. Reza [Department of Physics, Sharif University of Technology, Tehran 11155-9161 (Iran, Islamic Republic of); Institute of Physics, Carl von Ossietzky University, D-26111 Oldenburg (Germany); CNRS UMR 6202, Observatoire de la Cote d'Azur, BP 4229, 06304 Nice Cedex 4 (France)

    2009-01-09T23:59:59.000Z

    We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes.

  7. Integrated method for chaotic time series analysis

    DOE Patents [OSTI]

    Hively, L.M.; Ng, E.G.

    1998-09-29T23:59:59.000Z

    Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data are disclosed. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated. 8 figs.

  8. Integrated method for chaotic time series analysis

    DOE Patents [OSTI]

    Hively, Lee M. (Philadelphia, TN); Ng, Esmond G. (Concord, TN)

    1998-01-01T23:59:59.000Z

    Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

  9. Can biomass time series be reliably assessed from CPUE time series data Francis Lalo1

    E-Print Network [OSTI]

    Hawai'i at Manoa, University of

    1 Can biomass time series be reliably assessed from CPUE time series data only? Francis Laloë1 to abundance. This means (i) that catchability is constant and (ii) that all the biomass is catchable. If so, relative variations in CPUE indicate the same relative variations in biomass. Myers and Worm consider

  10. Time Series Models: Hidden Markov Models

    E-Print Network [OSTI]

    Roweis, Sam

    Time Series Models: Hidden Markov Models & Linear Dynamical Systems Sam Roweis Gatsby Computational before. Discrete state: { Moore and Mealy machines (engineering) { stochastic #12;nite state automata (CS chain with stochastic measurements. Gauss-Markov process in a pancake. PSfrag replacements x 1 y 1 x 2 y

  11. Time Series Models: Hidden Markov Models

    E-Print Network [OSTI]

    Roweis, Sam

    Time Series Models: Hidden Markov Models & Linear Dynamical Systems Sam Roweis Gatsby Computational. Discrete state: { Moore and Mealy machines (engineering) { stochastic #12;nite state automata (CS with stochastic measurements. Gauss-Markov process in a pancake. PSfrag replacements x 1 y 1 x 2 y 2 x 3 y 3 x T y

  12. Multilinear Dynamical Systems for Tensor Time Series

    E-Print Network [OSTI]

    Russell, Stuart

    of the stock prices of n multiple companies comprise a time series of 6 × n tensors. A grayscale video sequence ocean temperatures will increase. Prediction of stock prices may not only inform investors but also help to stabilize the economy and prevent market collapse. The relationships between particular subsets of tensor

  13. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Sripada, Yaji

    for generating textual summaries. Our algorithm has been implemented in a weather forecast generation system. 1 presentation, aid human understanding of the underlying data sets. SUMTIME is a research project aiming turbines. In the domain of meteorology, time series data produced by numerical weather prediction (NWP

  14. Some results of analysis of source position time series

    E-Print Network [OSTI]

    Malkin, Zinovy

    2015-01-01T23:59:59.000Z

    Source position time series produced by International VLBI Service for Geodesy and astrometry (IVS) Analysis Centers were analyzed. These series was computed using different software and analysis strategy. Comparison of this series showed that they have considerably different scatter and systematic behavior. Based on the inspection of all the series, new sources were identified as sources with irregular (non-random) position variations. Two statistics used to estimate the noise level in the time series, namely RMS and ADEV were compared.

  15. Exact Primitives for Time Series Data Mining

    E-Print Network [OSTI]

    Mueen, Abdullah Al

    2012-01-01T23:59:59.000Z

    G. Silva, and Rui M. M. Brito. Mining approximate motifs intime series. In Data Mining, 2001. ICDM 2001, Proceedingson Knowledge discovery and data mining, KDD, pages 947–956,

  16. Forecasting the underlying potential governing climatic time series

    E-Print Network [OSTI]

    Livina, V N; Mudelsee, M; Lenton, T M

    2012-01-01T23:59:59.000Z

    We introduce a technique of time series analysis, potential forecasting, which is based on dynamical propagation of the probability density of time series. We employ polynomial coefficients of the orthogonal approximation of the empirical probability distribution and extrapolate them in order to forecast the future probability distribution of data. The method is tested on artificial data, used for hindcasting observed climate data, and then applied to forecast Arctic sea-ice time series. The proposed methodology completes a framework for `potential analysis' of climatic tipping points which altogether serves anticipating, detecting and forecasting climate transitions and bifurcations using several independent techniques of time series analysis.

  17. 14.384 Time Series Analysis, Fall 2002

    E-Print Network [OSTI]

    Kuersteiner, Guido M.

    Theory and application of time series methods in econometrics, including representation theorems, decomposition theorems, prediction, spectral analysis, estimation with stationary and nonstationary processes, VARs, unit ...

  18. A Framework for Comparison of Spatiotemporal and Time Series...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Framework for Comparison of Spatiotemporal and Time Series Datasets NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy,...

  19. Efficient Mining of Partial Periodic Patterns in Time Series Database

    E-Print Network [OSTI]

    Dong, Guozhu

    Efficient Mining of Partial Periodic Patterns in Time Series Database In ICDE 99 Jiawei Han \\Lambda peri­ odic patterns in time­series databases, is an interesting data mining problem. Previous studies several algorithms for efficient mining of par­ tial periodic patterns, by exploring some interesting

  20. Discovering Ecosystem Models from Time-Series Data

    E-Print Network [OSTI]

    Langley, Pat

    Discovering Ecosystem Models from Time-Series Data Dileep George, 1 Kazumi Saito, 2 Pat Langley, 1. Ecosystem models are used to interpret and predict the in- teractions of species and their environment. In this paper, we address the task of inducing ecosystem models from background knowledge and time- series data

  1. Distribution Based Data Filtering for Financial Time Series Forecasting

    E-Print Network [OSTI]

    Bailey, James

    recent past. In this paper, we address the challenge of forecasting the behavior of time series using@unimelb.edu.au Abstract. Changes in the distribution of financial time series, particularly stock market prices, can of stock prices, which aims to forecast the future values of the price of a stock, in order to obtain

  2. Analysis of Time Series Using Compact Model-Based Descriptions

    E-Print Network [OSTI]

    Kriegel, Hans-Peter

    Analysis of Time Series Using Compact Model-Based Descriptions Hans-Peter Kriegel, Peer Kr this is a combination of the coefficients 1, . . . , 3 representing the three input time series using a function f-of-the-art compression methods. The results are visually presented in a very concise way so that the user can easily

  3. APPARENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION

    E-Print Network [OSTI]

    Gilbes, Fernando

    APPARENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION Roy A. Armstrong, Jose M of Puerto Rico Mayagüez, Puerto Rico 00681 ABSTRACT The Caribbean Time Series, located 28 nautical miles in near- surface waters of the northeastern Caribbean Basin. Apparent optical properties such as, remote

  4. IMPROVEMENTS TO THE RADIANT TIME SERIES METHOD COOLING LOAD CALCULATION

    E-Print Network [OSTI]

    IMPROVEMENTS TO THE RADIANT TIME SERIES METHOD COOLING LOAD CALCULATION PROCEDURE By BEREKET, Australia 1998 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial TO THE RADIANT TIME SERIES METHOD COOLING LOAD CALCULATION PROCEDURE Dissertation Approved: Dr. Jeffrey D

  5. Wavelet analysis and scaling properties of time series

    E-Print Network [OSTI]

    P. Manimaran; Prasanta K. Panigrahi; Jitendra C. Parikh

    2005-08-30T23:59:59.000Z

    We propose a wavelet based method for the characterization of the scaling behavior of non-stationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multi-fractal behavior.

  6. Estimation of connectivity measures in gappy time series

    E-Print Network [OSTI]

    Papadopoulos, G

    2015-01-01T23:59:59.000Z

    A new method is proposed to compute connectivity measures on multivariate time series with gaps. Rather than removing or filling the gaps, the rows of the joint data matrix containing empty entries are removed and the calculations are done on the remainder matrix. The method, called measure adapted gap removal (MAGR), can be applied to any connectivity measure that uses a joint data matrix, such as cross correlation, cross mutual information and transfer entropy. MAGR is favorably compared using these three measures to a number of known gap-filling techniques, as well as the gap closure. The superiority of MAGR is illustrated on time series from synthetic systems and financial time series.

  7. 14.384 Time Series Analysis, Fall 2007

    E-Print Network [OSTI]

    Mikusheva, Anna, 1976-

    The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain ...

  8. 14.384 Time Series Analysis, Fall 2008

    E-Print Network [OSTI]

    Schrimpf, Paul

    The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain ...

  9. Local prediction of turning points of oscillating time series

    E-Print Network [OSTI]

    D. Kugiumtzis

    2008-08-06T23:59:59.000Z

    For oscillating time series, the prediction is often focused on the turning points. In order to predict the turning point magnitudes and times it is proposed to form the state space reconstruction only from the turning points and modify the local (nearest neighbor) model accordingly. The model on turning points gives optimal prediction at a lower dimensional state space than the optimal local model applied directly on the oscillating time series and is thus computationally more efficient. Monte Carlo simulations on different oscillating nonlinear systems showed that it gives better predictions of turning points and this is confirmed also for the time series of annual sunspots and total stress in a plastic deformation experiment.

  10. Continuous Time Random Walks and South Spain Seismic Series

    E-Print Network [OSTI]

    A. Posadas; J. Morales; F. Vidal; O. Sotolongo-Costa; J. C. Antoranz

    2002-05-27T23:59:59.000Z

    Levy flights were introduced through the mathematical research of the algebra or random variables with infinite moments. Mandelbrot recognized that the Levy flight prescription had a deep connection to scale-invariant fractal random walk trajectories. The theory of Continuous Time Random Walks (CTRW) can be described in terms of Levy distribution functions and it can be used to explain some earthquake characteristics like the distribution of waiting times and hypocenter locations in a seismic region. This paper checks the validity of this assumption analyzing three seismic series localized in South Spain. The three seismic series (Alboran, Antequera and Loja) show qualitatively the same behavior, although there are quantitative differences between them.

  11. Fast and Flexible Multivariate Time Series Subsequence Search Kanishka Bhaduri

    E-Print Network [OSTI]

    Oza, Nikunj C.

    search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence approach" may include searching on parameters such as speed, descent rate, vertical flight pathFast and Flexible Multivariate Time Series Subsequence Search Kanishka Bhaduri MCT Inc., NASA ARC

  12. Time series of a CME blasting out from the Sun

    E-Print Network [OSTI]

    Christian, Eric

    #12;Time series of a CME blasting out from the Sun Composite image of the Sun in UV light with the naked eye, the Sun seems static, placid, constant. From the ground, the only notice- able variations in the Sun are its location (where will it rise and set today?) and its color (will clouds cover

  13. Wavelet Methods for Time Series Analysis Don Percival

    E-Print Network [OSTI]

    Percival, Don

    Wavelet Methods for Time Series Analysis Don Percival Applied Physics Laboratory Box 355640 in the morning and two in the afternoon, each about 45 minutes long) · Monday 1: introduction to wavelets and wavelet transforms (Part I) 2: introduction to the discrete wavelet transform (Part II) 3 & 4: basic

  14. Visual Analysis of Frequent Patterns In Large Time Series

    E-Print Network [OSTI]

    Ramakrishnan, Naren

    1 shows an example on how to monitor chiller efficiency in data centers using a pair of data center chiller time series in which different motifs were discovered. The illustrated process can be subdivided valued vector ti captures the data values (e.g., chiller utilization in the data center example), we

  15. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural dedicated models to forecast the 12 individual months directly. Results indicate better performance is superior to naïve forecasts based on persistence and seasonality, and is better than results quoted

  16. Mining Deviants in Time Series Data Streams S. Muthukrishnan

    E-Print Network [OSTI]

    Shah, Rahul

    outliers. There is a long history of study of various outliers in statistics and databases, and a recent aberrations. Deviants are known to be of great mining value in time series databases. We present first (highway, telephone, internet, web click), Supported by National Science foundation grants EIA 0087022

  17. Chaotic time series prediction using artificial neural networks

    SciTech Connect (OSTI)

    Bartlett, E.B.

    1991-12-31T23:59:59.000Z

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  18. Chaotic time series prediction using artificial neural networks

    SciTech Connect (OSTI)

    Bartlett, E.B.

    1991-01-01T23:59:59.000Z

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  19. Improving predictability of time series using maximum entropy methods

    E-Print Network [OSTI]

    Gregor Chliamovitch; Alexandre Dupuis; Bastien Chopard; Anton Golub

    2014-11-28T23:59:59.000Z

    We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, at least in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, for then it provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.

  20. Improving predictability of time series using maximum entropy methods

    E-Print Network [OSTI]

    Chliamovitch, Gregor; Chopard, Bastien; Golub, Anton

    2014-01-01T23:59:59.000Z

    We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, at least in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, for then it provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.

  1. Figure 5. Wavelet time series analysis for yearly LBM outbreaks. a) The normalized time-series. b) Temporally-local wavelet power spectrum (dark red indicates the strongest

    E-Print Network [OSTI]

    SUPPLEMENT Figure 5. Wavelet time series analysis for yearly LBM outbreaks. a) The normalized time-series. b) Temporally-local wavelet power spectrum (dark red indicates the strongest periodicity while white indicates the weakest periodicity). c) Spatiotemporally-global wavelet spectrum. d) Time-series plot

  2. De Bilt, 2012 | Technical Report ; TR-326 Time series transformation tool

    E-Print Network [OSTI]

    Stoffelen, Ad

    De Bilt, 2012 | Technical Report ; TR-326 Time series transformation tool: description #12;#12;Time series transformation tool: description of the program to generate time series consistent ransformation tool: description of the program to generate time series consistent with the KNMI'06 climate

  3. ARM: Millimeter Wave Cloud Radar (MMCR), replaces mmcrcal and mmcrmoments datastreams following C-40 processor upgrade of 2003.09.09

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Widener, Kevin; Bharadwaj, Nitin; Johnson, Karen

    Millimeter Wave Cloud Radar (MMCR), replaces mmcrcal and mmcrmoments datastreams following C-40 processor upgrade of 2003.09.09

  4. 1MaPhySto Workshop 9/04 Nonlinear Time Series ModelingNonlinear Time Series Modeling

    E-Print Network [OSTI]

    . "Stylized facts" concerning financial time series 4. ARCH and GARCH models 5. Forecasting with GARCH 6 of multivariate RV equivalence 8.5 examples 8.6 Extremes for GARCH and SV models 8.7 Summary of results for ACF of GARCH & SV models #12;4MaPhySto Workshop 9/04 Part III: Nonlinear and NonGaussian State-Space Models 1

  5. Characterizing Weak Chaos using Time Series of Lyapunov Exponents

    E-Print Network [OSTI]

    R. M. da Silva; C. Manchein; M. W. Beims; E. G. Altmann

    2015-06-13T23:59:59.000Z

    We investigate chaos in mixed-phase-space Hamiltonian systems using time series of the finite- time Lyapunov exponents. The methodology we propose uses the number of Lyapunov exponents close to zero to define regimes of ordered (stickiness), semi-ordered (or semi-chaotic), and strongly chaotic motion. The dynamics is then investigated looking at the consecutive time spent in each regime, the transition between different regimes, and the regions in the phase-space associated to them. Applying our methodology to a chain of coupled standard maps we obtain: (i) that it allows for an improved numerical characterization of stickiness in high-dimensional Hamiltonian systems, when compared to the previous analyses based on the distribution of recurrence times; (ii) that the transition probabilities between different regimes are determined by the phase-space volume associated to the corresponding regions; (iii) the dependence of the Lyapunov exponents with the coupling strength.

  6. State Space Reconstruction for Multivariate Time Series Prediction

    E-Print Network [OSTI]

    I. Vlachos; D. Kugiumtzis

    2008-09-12T23:59:59.000Z

    In the nonlinear prediction of scalar time series, the common practice is to reconstruct the state space using time-delay embedding and apply a local model on neighborhoods of the reconstructed space. The method of false nearest neighbors is often used to estimate the embedding dimension. For prediction purposes, the optimal embedding dimension can also be estimated by some prediction error minimization criterion. We investigate the proper state space reconstruction for multivariate time series and modify the two abovementioned criteria to search for optimal embedding in the set of the variables and their delays. We pinpoint the problems that can arise in each case and compare the state space reconstructions (suggested by each of the two methods) on the predictive ability of the local model that uses each of them. Results obtained from Monte Carlo simulations on known chaotic maps revealed the non-uniqueness of optimum reconstruction in the multivariate case and showed that prediction criteria perform better when the task is prediction.

  7. Time series modeling of autonomous hybrid power systems

    SciTech Connect (OSTI)

    Quinlan, P.J.; Beckman, W.A.; Mitchell, J.W.; Klein, S.A.; Blair, N.J. [Univ. of Wisconsin, Madison, WI (United States). Solar Energy Lab.

    1997-12-31T23:59:59.000Z

    The Solar Energy Laboratory (SEL) has developed a wind diesel PV hybrid systems simulator, UW-HYBRID 1.0, as an application of the TRNSYS 14.2 time-series simulation environment. The simulator provides a customizable user interface. The simulation provides an AC/DC buss, diesel generators, wind turbines, PV modules, a battery bank, and power converter. PV system simulations include solar angle and peak power tracking options. Diesel simulations include estimated fuel-use and waste heat output, and are dispatched using a least-cost of fuel strategy. Wind system simulations include varying air density, wind shear and wake effects. Time step duration is user-selectable. This paper provides a description of the simulation models and example output.

  8. Time series power flow analysis for distribution connected PV generation.

    SciTech Connect (OSTI)

    Broderick, Robert Joseph; Quiroz, Jimmy Edward; Ellis, Abraham; Reno, Matthew J. [Georgia Institute of Technology, Atlanta, GA; Smith, Jeff [Electric Power Research Institute, Knoxville, TN; Dugan, Roger [Electric Power Research Institute, Knoxville, TN

    2013-01-01T23:59:59.000Z

    Distributed photovoltaic (PV) projects must go through an interconnection study process before connecting to the distribution grid. These studies are intended to identify the likely impacts and mitigation alternatives. In the majority of the cases, system impacts can be ruled out or mitigation can be identified without an involved study, through a screening process or a simple supplemental review study. For some proposed projects, expensive and time-consuming interconnection studies are required. The challenges to performing the studies are twofold. First, every study scenario is potentially unique, as the studies are often highly specific to the amount of PV generation capacity that varies greatly from feeder to feeder and is often unevenly distributed along the same feeder. This can cause location-specific impacts and mitigations. The second challenge is the inherent variability in PV power output which can interact with feeder operation in complex ways, by affecting the operation of voltage regulation and protection devices. The typical simulation tools and methods in use today for distribution system planning are often not adequate to accurately assess these potential impacts. This report demonstrates how quasi-static time series (QSTS) simulation and high time-resolution data can be used to assess the potential impacts in a more comprehensive manner. The QSTS simulations are applied to a set of sample feeders with high PV deployment to illustrate the usefulness of the approach. The report describes methods that can help determine how PV affects distribution system operations. The simulation results are focused on enhancing the understanding of the underlying technical issues. The examples also highlight the steps needed to perform QSTS simulation and describe the data needed to drive the simulations. The goal of this report is to make the methodology of time series power flow analysis readily accessible to utilities and others responsible for evaluating potential PV impacts.

  9. TQuEST: Threshold Query Execution for Large Sets of Time Series

    E-Print Network [OSTI]

    Kriegel, Hans-Peter

    TQuEST: Threshold Query Execution for Large Sets of Time Series Johannes AÃ?falg, Hans-Peter Kriegel TQuEST, a powerful query processor for time series databases. TQuEST supports a novel but very useful times. 1 Introduction In this paper, we present TQuEST, a powerful analysis tool for time series

  10. Some methods and models for analyzing time-series gene expression data

    E-Print Network [OSTI]

    Jammalamadaka, Arvind K. (Arvind Kumar), 1981-

    2009-01-01T23:59:59.000Z

    Experiments in a variety of fields generate data in the form of a time-series. Such time-series profiles, collected sometimes for tens of thousands of experiments, are a challenge to analyze and explore. In this work, ...

  11. Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

    E-Print Network [OSTI]

    Lin, Ying-Tsong

    In this paper, a method for merging partial overlapping time series of ocean profiles into a single time series of profiles using empirical orthogonal function (EOF) decomposition with the objective analysis is presented. ...

  12. A Multivariate Time Series Method for Monte Carlo Reactor Analysis

    SciTech Connect (OSTI)

    Taro Ueki

    2008-08-14T23:59:59.000Z

    A robust multivariate time series method has been established for the Monte Carlo calculation of neutron multiplication problems. The method is termed Coarse Mesh Projection Method (CMPM) and can be implemented using the coarse statistical bins for acquisition of nuclear fission source data. A novel aspect of CMPM is the combination of the general technical principle of projection pursuit in the signal processing discipline and the neutron multiplication eigenvalue problem in the nuclear engineering discipline. CMPM enables reactor physicists to accurately evaluate major eigenvalue separations of nuclear reactors with continuous energy Monte Carlo calculation. CMPM was incorporated in the MCNP Monte Carlo particle transport code of Los Alamos National Laboratory. The great advantage of CMPM over the traditional Fission Matrix method is demonstrated for the three space-dimensional modeling of the initial core of a pressurized water reactor.

  13. Predictive Mining of Time Series Data in Astronomy

    E-Print Network [OSTI]

    E. Perlman; A. Java

    2002-12-18T23:59:59.000Z

    We discuss the development of a Java toolbox for astronomical time series data. Rather than using methods conventional in astronomy (e.g., power spectrum and cross-correlation analysis) we employ rule discovery techniques commonly used in analyzing stock-market data. By clustering patterns found within the data, rule discovery allows one to build predictive models, allowing one to forecast when a given event might occur or whether the occurrence of one event will trigger a second. We have tested the toolbox and accompanying display tool on datasets (representing several classes of objects) from the RXTE All Sky Monitor. We use these datasets to illustrate the methods and functionality of the toolbox. We also discuss issues that can come up in data analysis as well as the possible future development of the package.

  14. Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial

    E-Print Network [OSTI]

    Shen, Haipeng

    Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial Spline Approach of functional coefficient regression models for non-linear time series. Consistency and rate of convergence regression model extends several familiar non-linear time series models such as the exponential

  15. Analysis of Geophysical Time Series Using Discrete Wavelet Transforms: An Overview

    E-Print Network [OSTI]

    Percival, Don

    Analysis of Geophysical Time Series Using Discrete Wavelet Transforms: An Overview Donald B geophysical time series. The basic idea is to transform a time series into coefficients describing how in geophysical data analysis. The intent of this article is to give an overview of how DWTs can be used

  16. Geospatial analysis of vulnerable beach-foredune systems from decadal time series of lidar data

    E-Print Network [OSTI]

    Mitasova, Helena

    Geospatial analysis of vulnerable beach-foredune systems from decadal time series of lidar data, Geospatial analysis of vulnerable beach- foredune systems from decadal time series of lidar data, Journal densities; therefore, geospatial analysis, when applied to decadal lidar time series, needs to address

  17. QUASI--MAXIMUM--LIKELIHOOD ESTIMATION IN HETEROSCEDASTIC TIME SERIES: A STOCHASTIC RECURRENCE EQUATIONS APPROACH

    E-Print Network [OSTI]

    Mikosch, Thomas

    . The resulting theory is applied to popular financial time series models: GARCH(1, 1), asymmetric GARCH(1, 1 for a general class of heteroscedastic time series models, which includes GARCH(1, 1). Recall that the time series (X t ) is called a GARCH(p, q) (generalized autoregressive conditionally heteroscedastic) process

  18. Filtering out high frequencies in time series using F-transform$

    E-Print Network [OSTI]

    Kreinovich, Vladik

    Filtering out high frequencies in time series using F-transform$ Vil´em Nov´akc , Irina Perfilievac) Preprint submitted to Elsevier February 10, 2013 #12;Filtering out high frequencies in time series using F at El Paso 500 W. University, El Paso, TX 79968, USA This paper is devoted to analysis of time series

  19. Filtering out high frequencies in time series using F-transform$

    E-Print Network [OSTI]

    Kreinovich, Vladik

    Filtering out high frequencies in time series using F-transform$ Vil´em Nov´akc , Irina Perfilievac) Preprint submitted to Elsevier February 3, 2014 #12;Filtering out high frequencies in time series using F, El Paso, TX 79968, USA 1. Introduction This paper is devoted to analysis of time series using fuzzy

  20. Abstract--Meteorological time series are characterized by important spatial and temporal variation. Model determination and

    E-Print Network [OSTI]

    Fernandez, Thomas

    of the meteorological time series used, which includes the use of statistical techniques to detect whether there exist for the time series using an evolutionary algorithm that adaptively adjusts some of its parameters during its and temperatures collected in a region of Romania. The results are promising for the analysis of such time series

  1. Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences

    E-Print Network [OSTI]

    Dominici, Francesca

    1 Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences S in time-series studies 1 11/11/99 Keywords: measurement error, air pollution, time series, exposure of air pollution and health. Because measurement error may have substantial implications for interpreting

  2. New Problems for an Old Design: Time-Series Analyses of Air Pollution and Health

    E-Print Network [OSTI]

    Dominici, Francesca

    New Problems for an Old Design: Time-Series Analyses of Air Pollution and Health Jonathan M. Samet1 of particulate air pollution on the same or recent days (1;2). Studies of similar time-series design of morbidity for adverse effects of particulate air pollution on the public's health. The daily time-series studies of air

  3. SumTime-Turbine: A Knowledge-Based System to Communicate Gas Turbine Time-Series Data

    E-Print Network [OSTI]

    Reiter, Ehud

    SumTime-Turbine: A Knowledge-Based System to Communicate Gas Turbine Time-Series Data Jin Yu of Aberdeen Aberdeen, AB24 3UE, UK {jyu, ereiter, jhunter, ssripada}@csd.abdn.ac.uk Abstract: SumTime-Turbine produces textual summaries of archived time- series data from gas turbines. These summaries should help

  4. Estimating the predictability of economic and financial time series

    E-Print Network [OSTI]

    Quentin Giai Gianetto; Jean-Marc Le Caillec; Erwan Marrec

    2012-12-12T23:59:59.000Z

    The predictability of a time series is determined by the sensitivity to initial conditions of its data generating process. In this paper our goal is to characterize this sensitivity from a finite sample by assuming few hypotheses on the data generating model structure. In order to measure the distance between two trajectories induced by a same noisy chaotic dynamic from two close initial conditions, a symmetric Kullback-Leiber divergence measure is used. Our approach allows to take into account the dependence of the residual variance on initial conditions. We show it is linked to a Fisher information matrix and we investigated its expressions in the cases of covariance-stationary processes and ARCH($\\infty$) processes. Moreover, we propose a consistent non-parametric estimator of this sensitivity matrix in the case of conditionally heteroscedastic autoregressive nonlinear processes. Various statistical hypotheses can so be tested as for instance the hypothesis that the data generating process is "almost" independently distributed at a given moment. Applications to simulated data and to the stock market index S&P500 illustrate our findings. More particularly, we highlight a significant relationship between the sensitivity to initial conditions of the daily returns of the S&P 500 and their volatility.

  5. Early Classification of Multivariate Time Series Using a Hybrid HMM/SVM model

    E-Print Network [OSTI]

    Obradovic, Zoran

    Early Classification of Multivariate Time Series Using a Hybrid HMM/SVM model Mohamed F. Ghalwash to use a shorter time interval for classification is often more favorable than having a slightly more with other models that use full time series both in training and testing. Analysis of biomedical data has

  6. The level crossing analysis of German stock market index (DAX) and daily oil price time series

    E-Print Network [OSTI]

    Shayeganfar, F; Peinke, J; Tabar, M Reza Rahimi

    2010-01-01T23:59:59.000Z

    The level crossing analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, $\

  7. Image/Time Series Mining Algorithms: Applications to Developmental Biology, Document Processing and Data Streams

    E-Print Network [OSTI]

    Tataw, Oben Moses

    2013-01-01T23:59:59.000Z

    International Conference on Data Mining (2001). Khairy, K. ,and Eamonn Keogh (2011). Mining Historical Documents forWang, E. J. Keogh. Querying and mining of time series data.

  8. 1Banff 6/06 Structural Break Detection in Time Series ModelsStructural Break Detection in Time Series Models

    E-Print Network [OSTI]

    breaks in this series? #12;5Banff 6/06 Introduction Examples AR GARCH Stochastic volatility State space Simulation results Applications Simulation results for GARCH and SV models #12;6Banff 6/06 Examples 1 ),,( 1 jjpj K #12;7Banff 6/06 Examples (cont) 2. Segmented GARCH model: where 0 = 1

  9. 1NCAR-IMAGe 2006 Structural Break Detection in Time Series ModelsStructural Break Detection in Time Series Models

    E-Print Network [OSTI]

    -202 Any breaks in this series? #12;5NCAR-IMAGe 2006 Introduction Examples AR GARCH Stochastic volatility break estimation Simulation results Applications Simulation results for GARCH and SV models #12;6NCAR-tjptjptjjt tYYY jj GARCH model

  10. Lean Blow-Out Prediction in Gas Turbine Combustors Using Symbolic Time Series Analysis

    E-Print Network [OSTI]

    Ray, Asok

    Lean Blow-Out Prediction in Gas Turbine Combustors Using Symbolic Time Series Analysis Achintya of lean blowout in gas turbine combustors based on symbolic analysis of time series data from optical. For the purpose of detecting lean blowout in gas turbine combustors, the state probability vector obtained

  11. Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques

    E-Print Network [OSTI]

    Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Aachen University, Germany {kremer, guennemann, seidl}@cs.rwth-aachen.de Abstract--Climate change can series, and trace the clusters over time. A climate pattern is categorized as a changing pattern

  12. Two problems with variational expectation maximisation for time-series models

    E-Print Network [OSTI]

    Ghahramani, Zoubin

    optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of v as a variational optimisation of a free-energy (Hathaway, 1986; Neal and Hinton, 1998). Consider observationsChapter 1 Two problems with variational expectation maximisation for time-series models Richard

  13. Wavelet Methods for Time Series Analysis Half-Day Workshop Presented at UNSW

    E-Print Network [OSTI]

    Percival, Don

    Wavelet Methods for Time Series Analysis Half-Day Workshop Presented at UNSW Don Percival Visiting://faculty.washington.edu/dbp Overview of Workshop · two sessions, each 1 hour and 45 minutes long I: introduction to wavelets and wavelet transforms II: wavelet-based statistical analysis of time series - wavelet variance (also known

  14. Wavelet Methods for Time Series Analysis Half-Day Workshop Presented at UQ St Lucia Campus

    E-Print Network [OSTI]

    Percival, Don

    Wavelet Methods for Time Series Analysis Half-Day Workshop Presented at UQ St Lucia Campus Don://faculty.washington.edu/dbp Overview of Workshop · two sessions, each 1 hour and 45 minutes long I: introduction to wavelets and wavelet transforms II: wavelet-based statistical analysis of time series - wavelet variance (also known

  15. Time series modeling and large scale global solar radiation forecasting from geostationary satellites data

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Time series modeling and large scale global solar radiation forecasting from geostationary global solar radiation. In this paper, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour. The results presented in this paper relate to a particular territory

  16. Fast Bootstrap applied to LS-SVM for Long Term Prediction of Time Series

    E-Print Network [OSTI]

    Verleysen, Michel

    Fast Bootstrap applied to LS-SVM for Long Term Prediction of Time Series Amaury Lendasse HUT, CIS the Fast Bootstrap methodology introduced in previous works. I. INTRODUCTION Time series forecasting are based on resampling, as k-fold cross-validation, leave-one-out, and bootstrap [4]. Although they differ

  17. The moving blocks bootstrap versus parametric time series Richard M. Vogel

    E-Print Network [OSTI]

    Vogel, Richard M.

    The moving blocks bootstrap versus parametric time series models Richard M. Vogel Department adding uncertainty to the analysis. The moving blocks bootstrap is a simple resampling algorithm which of the moving block length. The moving blocks bootstrap resamples the observed time series using approximately

  18. A test for second order stationarity of a time series based on the Discrete Fourier Transform

    E-Print Network [OSTI]

    Subba Rao, Suhasini

    A test for second order stationarity of a time series based on the Discrete Fourier Transform property, we construct a Portmanteau type test statistic for testing stationarity of the time series. It is shown that under the null of stationarity, the test statistic is approximately a chi square distribution

  19. Discrimination and Classification of Nonstationary Time Series Using the SLEX Model

    E-Print Network [OSTI]

    Discrimination and Classification of Nonstationary Time Series Using the SLEX Model Hsiao-Yun HUANG a discriminant scheme based on the SLEX (smooth localized complex exponential) library. The SLEX library forms domains. Thus, the SLEX library has the ability to extract local spectral features of the time series

  20. Discrimination and Classification of Nonstationary Time Series using the SLEX Model

    E-Print Network [OSTI]

    Discrimination and Classification of Nonstationary Time Series using the SLEX Model Hsiao-Yun Huang scheme based on the SLEX (Smooth Localized Complex EXponential) library. The SLEX library forms domains. Thus, the SLEX library has the ability to extract local spectral features of the time series

  1. DETERMINING THE FRACTAL DIMENSION OF A TIME SERIES WITH A NEURAL NET

    E-Print Network [OSTI]

    Danon, Yaron

    DETERMINING THE FRACTAL DIMENSION OF A TIME SERIES WITH A NEURAL NET MARK J. EMBRECHTS AND YARON and require expert interaction for interpreting the calculated fractal dimension. Artificial neural nets (ANN) offer a fast and elegant way to estimate the fractal dimension of a time series. A backpropagation net

  2. iSAX: disk-aware mining and indexing of massive time series datasets

    E-Print Network [OSTI]

    Shieh, Jin; Keogh, Eamonn

    2009-01-01T23:59:59.000Z

    on both indexing and data mining problems. Finally, in Sect.0125-6 iSAX: disk-aware mining and indexing of massive timeCurrent research in indexing and mining time series data has

  3. Modelling signal interactions with application to financial time series

    E-Print Network [OSTI]

    Jain, Bonny

    2014-01-01T23:59:59.000Z

    In this thesis, we concern ourselves with the problem of reasoning over a set of objects evolving over time that are coupled through interaction structures that are themselves changing over time. We focus on inferring ...

  4. A New Architecture for Summarising Time Series Data

    E-Print Network [OSTI]

    Sripada, Yaji

    of the systems developed in the SumTime Project2 ) summarises sensor data from gas turbines. This is challenging because of the large amount of data being summarised; a typical gas turbine has 250 ana- logue data generation techniques to produce summaries of such data. A short extract from SumTime-Turbine's input data

  5. IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY

    E-Print Network [OSTI]

    Dominici, Francesca

    IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY Francesca Dominici series analyses of air pollution and health attracted the attention of the scientific community, policy makers, the press, and the diverse stakeholders con- cerned with air pollution. As the Environmental

  6. Introduction to (Generalized) Autoregressive Conditional Heteroskedasticity Models in Time Series

    E-Print Network [OSTI]

    Morrow, James A.

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4 ARCH/GARCH models 8 4.1 Sample Application and application of the ARCH/GARCH models proposed in the 1980's by econometricians such as Robert Engle (who won at the time). In particular, we focus on the paper, "GARCH 101: The Use of ARCH/GARCH Models in Applied Econo

  7. Fractal, entropic and chaotic approaches to complex physiological time series analysis: a critical appraisal

    E-Print Network [OSTI]

    Wu, Guo-Qiang

    A wide variety of methods based on fractal, entropic or chaotic approaches have been applied to the analysis of complex physiological time series. In this paper, we show that fractal and entropy measures are poor indicators ...

  8. Essays on empirical time series modeling with causality and structural change 

    E-Print Network [OSTI]

    Kim, Jin Woong

    2006-10-30T23:59:59.000Z

    In this dissertation, three related issues of building empirical time series models for financial markets are investigated with respect to contemporaneous causality, dynamics, and structural change. In the first essay, nation-wide industry...

  9. Mining Time Series Data: Moving from Toy Problems to Realistic Deployments

    E-Print Network [OSTI]

    Hu, Bing

    2013-01-01T23:59:59.000Z

    Conference on Data Mining, 2010 V. Chandola, A. Banerjee,and E. Keogh. “ Querying and Mining of Time Series Data:2 nd Workshop on Temporal Data Mining, 2002 K. Malatesta, S.

  10. Analysis of MODIS 250 m NDVI Using Different Time-Series Data for Crop Type Separability

    E-Print Network [OSTI]

    Lee, Eunmok

    2014-08-31T23:59:59.000Z

    The primary objectives of this research were to: (1) investigate the use of different compositing periods of NDVI values of time-series MODIS 250 m data for distinguishing major crop types on the central Great Plains of ...

  11. Using temporal averaging to decouple annual and nonannual information in AVHRR NDVI time series

    E-Print Network [OSTI]

    Kastens, Jude Heathcliff; Lerner, David E.; Jakubauskas, Mark E.

    2003-11-01T23:59:59.000Z

    As regularly spaced time series imagery becomes more prevalent in the remote sensing community, monitoring these data for temporal consistency will become an increasingly important problem. Long-term trends must be identified, and it must...

  12. NONLINEAR TIME SERIES MODEL FOR VBR VIDEO TRAFFIC JIMMIE L. DAVIS, KAVITHA CHANDRA AND CHARLES THOMPSON

    E-Print Network [OSTI]

    Chandra, Kavitha

    THOMPSON Center for Advanced Computation and Telecommunications University of Massachusetts Lowell One, nonlinear time-series Corresponding author: Charles Thompson; charles_thompson2@uml.edu 1 INTRODUCTION

  13. The Spectral Density Estimation of Stationary Time Series with Missing Data

    E-Print Network [OSTI]

    Schellekens, Michel P.

    reported in literature (see, e.g. Green et al., 2002, Kaneoke and Vitek, 1996, Fortin and Mackey, 1999, and Laguna et al., 1998). Here we consider estimating the spectral density of stationary time series

  14. A FAST MODEL-BUILDING METHOD FOR TIME SERIES USING GENETIC PROGRAMMING

    E-Print Network [OSTI]

    Fernandez, Thomas

    A FAST MODEL-BUILDING METHOD FOR TIME SERIES USING GENETIC PROGRAMMING I. Yoshihara Faculty) financial problems e.g. stock price indices and gold prices. The experiments lead us to the conclusion

  15. Dynamic dependence analysis : modeling and inference of changing dependence among multiple time-series

    E-Print Network [OSTI]

    Siracusa, Michael Richard, 1980-

    2009-01-01T23:59:59.000Z

    In this dissertation we investigate the problem of reasoning over evolving structures which describe the dependence among multiple, possibly vector-valued, time-series. Such problems arise naturally in variety of settings. ...

  16. EXPERIMENTAL VALIDATION OF THE RADIANT TIME SERIES METHOD FOR COOLING LOAD

    E-Print Network [OSTI]

    EXPERIMENTAL VALIDATION OF THE RADIANT TIME SERIES METHOD FOR COOLING LOAD CALCULATIONS By IP SENG College of the Oklahoma State University in partial fulfillment of the requirements for the Degree LOAD CALCULATIONS Thesis Approved: _______________________________________ Thesis Advisor

  17. The relation between Brazilian and Chicago Board of Trade soybean prices: a time series test

    E-Print Network [OSTI]

    Melcher, Bruno

    1991-01-01T23:59:59.000Z

    THE RELATION BETWEEN BRAZILIAN AND CHICAGO BOARD OF TRADE SOYBEAN PRICES ? A TIME SERIES TEST A Thesis BRUNO MELCHER Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of MASTER OF SCIENCE May 1991 Major Subject: Agricultural Economics THE RELATION BETWEEN BRAZILIAN AND CHICAGO BOARD OF TRADE SOYBEAN PRICES ? A TIME SERIES TEST A Thesis by BRUNO MELCHER Approved as to style and content by: ' f J David...

  18. The application of complex network time series analysis in turbulent heated jets

    SciTech Connect (OSTI)

    Charakopoulos, A. K.; Karakasidis, T. E., E-mail: thkarak@uth.gr; Liakopoulos, A. [Laboratory of Hydromechanics and Environmental Engineering, Department of Civil Engineering, University of Thessaly, 38334 Volos (Greece)] [Laboratory of Hydromechanics and Environmental Engineering, Department of Civil Engineering, University of Thessaly, 38334 Volos (Greece); Papanicolaou, P. N. [School of Civil Engineering, Department of Water Resources and Environmental Engineering, National Technical University of Athens, 5 Heroon Polytechniou St., 15780 Zografos (Greece)] [School of Civil Engineering, Department of Water Resources and Environmental Engineering, National Technical University of Athens, 5 Heroon Polytechniou St., 15780 Zografos (Greece)

    2014-06-15T23:59:59.000Z

    In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.

  19. Multiple Alignment of Continuous Time Series Jennifer Listgarten + , Radford M. Neal + , Sam T. Roweis + and Andrew Emili #

    E-Print Network [OSTI]

    Roweis, Sam

    Multiple Alignment of Continuous Time Series Jennifer Listgarten + , Radford M. Neal + , Sam T of continuous­valued time series from a stochastic process often contain systematic variations in rate time series generated by a noisy, stochastic process, large sys­ tematic sources of variability

  20. Iterative prediction of chaotic time series using a recurrent neural network

    SciTech Connect (OSTI)

    Essawy, M.A.; Bodruzzaman, M. [Tennessee State Univ., Nashville, TN (United States). Dept. of Electrical and Computer Engineering; Shamsi, A.; Noel, S. [USDOE Morgantown Energy Technology Center, WV (United States)

    1996-12-31T23:59:59.000Z

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neutral network used should be capable of modeling the highly non-linear behavior and the multi-attractor nature of such systems. In this paper the authors use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

  1. Proceedings of Student Research Day, CSIS, Pace University, May 9th, 2003 Modeling Economic Time Series Using a Focused Time Lagged

    E-Print Network [OSTI]

    Tappert, Charles

    Series Using a Focused Time Lagged FeedForward Neural Network N. Moseley ABSTRACT, - Artificial neural other series expansion.[2]. The motivation for analysis of time series using neural netwoProceedings of Student Research Day, CSIS, Pace University, May 9th, 2003 Modeling Economic Time

  2. Bayesian classification of partially observed outbreaks using time-series data.

    SciTech Connect (OSTI)

    Safta, Cosmin; Ray, Jaideep; Crary, David (Applied Research Associates, Inc, Arlington, VA); Cheng, Karen (Applied Research Associates, Inc, Arlington, VA)

    2010-05-01T23:59:59.000Z

    Results show that a time-series based classification may be possible. For the test cases considered, the correct model can be selected and the number of index case can be captured within {+-} {sigma} with 5-10 days of data. The low signal-to-noise ratio makes the classification difficult for small epidemics. The problem statement is: (1) Create Bayesian techniques to classify and characterize epidemics from a time-series of ICD-9 codes (will call this time-series a 'morbidity stream'); and (2) It is assumed the morbidity stream has already set off an alarm (through a Kalman filter anomaly detector) Starting with a set of putative diseases: Identify which disease or set of diseases 'fit the data best' and, Infer associated information about it, i.e. number of index cases, start time of the epidemic, spread rate, etc.

  3. Anomaly detection in thermal pulse combustors using symbolic time series analysis

    E-Print Network [OSTI]

    Ray, Asok

    339 Anomaly detection in thermal pulse combustors using symbolic time series analysis S Gupta1 for anomaly detection in thermal pulse combustors. The anomaly detection method has been tested on the time pulse combustor. Results are presented to exemplify early detection of combustion instability due

  4. Visualizing Frequent Patterns in Large Multivariate Time Series , M. Marwah1

    E-Print Network [OSTI]

    Ramakrishnan, Naren

    languages, detecting anomalies in patients' medical records over time [5], and chiller efficiency in data centers [14]. Figure 1 shows an example of the visual analysis of a pair of data center chiller time series in which different motifs were discovered. A chiller is a key component of the cooling

  5. Forecasting of preprocessed daily solar radiation time series using neural networks

    E-Print Network [OSTI]

    Boyer, Edmond

    Forecasting of preprocessed daily solar radiation time series using neural networks Christophe prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE ~ 21 t or at day d and year y d H0 Extraterrestrial solar radiation coefficient for day d [MJ/m²] xt, xd,y Time

  6. Representing and Utilizing Changing Historical Places as an Ontology Time Series

    E-Print Network [OSTI]

    Hyvönen, Eero

    Chapter 1 Representing and Utilizing Changing Historical Places as an Ontology Time Series Eero Hyv.g. Check Republic or Slo- vakia) or overlapping historic names of different times (e.g. Roman Empire interfaces. The system has been applied in the semantic cultural heritage portal CULTURESAMPO for semantic

  7. Melting of small Arctic ice caps observed from ERS scatterometer time series

    E-Print Network [OSTI]

    Smith, Laurence C.

    Melting of small Arctic ice caps observed from ERS scatterometer time series Laurence C. Smith,1 of melt onset can be observed over small ice caps, as well as the major ice sheets and multi-year sea ice for 14 small Arctic ice caps from 1992­2000. Interannual and regional variability in the timing of melt

  8. Applications of Universal Source Coding to Statistical Analysis of Time Series

    E-Print Network [OSTI]

    Ryabko, Boris

    2008-01-01T23:59:59.000Z

    We show how universal codes can be used for solving some of the most important statistical problems for time series. By definition, a universal code (or a universal lossless data compressor) can compress any sequence generated by a stationary and ergodic source asymptotically to the Shannon entropy, which, in turn, is the best achievable ratio for lossless data compressors. We consider finite-alphabet and real-valued time series and the following problems: estimation of the limiting probabilities for finite-alphabet time series and estimation of the density for real-valued time series, the on-line prediction, regression, classification (or problems with side information) for both types of the time series and the following problems of hypothesis testing: goodness-of-fit testing, or identity testing, and testing of serial independence. It is important to note that all problems are considered in the framework of classical mathematical statistics and, on the other hand, everyday methods of data compression (or ar...

  9. TIME SERIES MODELS OF THREE SETS OF RXTE OBSERVATIONS OF 4U 1543-47

    SciTech Connect (OSTI)

    Koen, C. [Department of Statistics, University of the Western Cape, Private Bag X17, Bellville, 7535 Cape (South Africa)] [Department of Statistics, University of the Western Cape, Private Bag X17, Bellville, 7535 Cape (South Africa)

    2013-03-01T23:59:59.000Z

    The X-ray nova 4U 1543-47 was in a different physical state (low/hard, high/soft, and very high) during the acquisition of each of the three time series analyzed in this paper. Standard time series models of the autoregressive moving average (ARMA) family are fitted to these series. The low/hard data can be adequately modeled by a simple low-order model with fixed coefficients, once the slowly varying mean count rate has been accounted for. The high/soft series requires a higher order model, or an ARMA model with variable coefficients. The very high state is characterized by a succession of 'dips', with roughly equal depths. These seem to appear independently of one another. The underlying stochastic series can again be modeled by an ARMA form, or roughly as the sum of an ARMA series and white noise. The structuring of each model in terms of short-lived aperiodic and 'quasi-periodic' components is discussed.

  10. Nonlinear analysis of time series of vibration data from a friction brake: SSA, PCA, and MFDFA

    E-Print Network [OSTI]

    Nikolay K. Vitanov; Norbert P. Hoffmann; Boris Wernitz

    2014-10-23T23:59:59.000Z

    We use the methodology of singular spectrum analysis (SSA), principal component analysis (PCA), and multi-fractal detrended fluctuation analysis (MFDFA), for investigating characteristics of vibration time series data from a friction brake. SSA and PCA are used to study the long time-scale characteristics of the time series. MFDFA is applied for investigating all time scales up to the smallest recorded one. It turns out that the majority of the long time-scale dynamics, that is presumably dominated by the structural dynamics of the brake system, is dominated by very few active dimensions only and can well be understood in terms of low dimensional chaotic attractors. The multi-fractal analysis shows that the fast dynamical processes originating in the friction interface are in turn truly multi-scale in nature.

  11. APPLICATION OF TEMPORAL TEXTURE FEATURES TO AUTOMATED ANALYSIS OF PROTEIN SUBCELLULAR LOCATIONS IN TIME SERIES FLUORESCENCE

    E-Print Network [OSTI]

    Gordon, Geoffrey J.

    are in constant movement within the cell, we extended our studies to time series images, which contain both to identify a protein's subcellular location is to label it with fluorescent dye, take microscope images this last step. The automated approach is more objective and sensitive than visual examination, and single

  12. OUTPUT-ONLY STATISTICAL TIME SERIES METHODS FOR STRUCTURAL HEALTH MONITORING: A COMPARATIVE STUDY

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    OUTPUT-ONLY STATISTICAL TIME SERIES METHODS FOR STRUCTURAL HEALTH MONITORING: A COMPARATIVE STUDY-STSMs) for Structural Health Monitoring (SHM) is presented via damage de- tection and identification in a GARTEUR type for Structural Health Monitoring (SHM). Their use is of high importance for structures such as bridges, aircraft

  13. A regression model with a hidden logistic process for feature extraction from time series

    E-Print Network [OSTI]

    Chamroukhi, Faicel

    operation. The switch operations signals can be seen as time series presenting non-linearities and various changes in regime. Basic linear regression can not be adopted for this type of sig- nals because a constant linear relationship is not adapted. As alternative to linear regression, some authors use

  14. Efficient Time Series Matching by Wavelets Kinpong Chan and Ada Waichee Fu

    E-Print Network [OSTI]

    Fu, Ada Waichee

    since the effectiveness of power concentration of a partic­ ular transformation depends on the nature to other problems. While large pieces reduce the power of multi­resolution, small pieces has weakness­Trees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series

  15. DAMAGE DETECTION IN A WIND TURBINE BLADE BASED ON TIME SERIES Simon Hoell, Piotr Omenzetter

    E-Print Network [OSTI]

    Boyer, Edmond

    DAMAGE DETECTION IN A WIND TURBINE BLADE BASED ON TIME SERIES METHODS Simon Hoell, Piotr Omenzetter, the consequences are growing sizes of wind turbines (WTs) and erections in remote places, such as off in the past years, thus efficient energy harvesting becomes more important. For the sector of wind energy

  16. Time Series Methods for ForecastingElectricityMarket Pricing Zoran Obradovic Kevin Tomsovic

    E-Print Network [OSTI]

    Obradovic, Zoran

    tested by attempting to capture relationships between present and past share prices using simpleTime Series Methods for ForecastingElectricityMarket Pricing Zoran Obradovic Kevin Tomsovic PO Box the predictability of electricity price under new market regulations and the engineering aspects of large scale

  17. Closing the carbon budget of estuarine wetlands with tower-based measurements and MODIS time series

    E-Print Network [OSTI]

    Chen, Jiquan

    Closing the carbon budget of estuarine wetlands with tower-based measurements and MODIS time series, Institute of Biodiversity Science, Fudan University, Shanghai 200433, China, wDepartment of Environmental have distinct carbon flux dynamics ­ the lateral carbon flux incurred by tidal activities, and methane

  18. Recognising Visual Patterns to Communicate Gas Turbine Time-Series Data

    E-Print Network [OSTI]

    Reiter, Ehud

    Recognising Visual Patterns to Communicate Gas Turbine Time-Series Data Jin Yu, Jim Hunter, Ehud analogue channels are sampled once per second and archived by the Tiger system for monitoring gas turbines is the generation of textual summaries. We are developing a knowledge-based system to summarise such data in the gas

  19. RESEARCH ARTICLE Time series analysis of infrared satellite data for detecting

    E-Print Network [OSTI]

    Wright, Robert

    successfully detected ther- mal anomalies in TIR data from the Advanced Very High Resolution Radiometer (AVHRR algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy. These instruments provide data over potentially dangerous, high-temperature phenomena, such as volcanic eruptions

  20. Indexing of Time Series by Major Minima and Maxima Eugene Fink

    E-Print Network [OSTI]

    Fink, Eugene

    sets: stock prices, air and sea temperatures, and wind speeds. Keywords: Compression, indexing.ics.uci.edu). Wind speeds: We have used daily wind speeds at twelve sites in Ireland, from 1961 to 1978, ob­ tained. Indexing: The indexing of a time­series database is based on the notion of major inclines, illustrated

  1. Indexing of Time Series by Major Minima and Maxima Eugene Fink

    E-Print Network [OSTI]

    Fink, Eugene

    sets: stock prices, air and sea temperatures, and wind speeds. Keywords: Compression, indexing.ics.uci.edu). Wind speeds: We have used daily wind speeds at twelve sites in Ireland, from 1961 to 1978, ob- tained. Indexing: The indexing of a time-series database is based on the notion of major inclines, illustrated

  2. INHERENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION (CaTS)

    E-Print Network [OSTI]

    Gilbes, Fernando

    INHERENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION (CaTS) Fernando Gilbes Rico 00681 ABSTRACT The temporal variability of the inherent water optical properties at the Caribbean wavelengths, but in all cases, the values were less than one. The correlation between bio-optical properties

  3. EOF analysis of a time series with application to tsunami detection

    E-Print Network [OSTI]

    Tolkova, Elena

    determines the accuracy of any forecast of the future tsunami evolution. A tsunami wave in the open ocean isEOF analysis of a time series with application to tsunami detection Elena Tolkova a, a. Decomposition of a tsunami buoy record in a functional space of tidal EOFs presents an efficient tool

  4. Multi-Resolution K-Means Clustering of Time Series and Application to Images

    E-Print Network [OSTI]

    Lin, Jessica

    Multi-Resolution K-Means Clustering of Time Series and Application to Images Michail Vlachos using orthonormal decompositions, we present an anytime version of the k-Means algorithm. The algorithm centers for k-Means is mitigated by assigning the final centers at each approximation level as the initial

  5. Global SunFarm Data Acquisition Network, Energy CRADLE, and Time Series Analysis

    E-Print Network [OSTI]

    Rollins, Andrew M.

    outdoor test beds across the world. Energy CRADLE is an ontology driven database acquisition tool which for Energy Technology workshop[1], the topic of photovoltaics(PV) lifetime and degradation science (LGlobal SunFarm Data Acquisition Network, Energy CRADLE, and Time Series Analysis Yang Hu, Mohammad

  6. CHANGE OF STRUCTURE IN FINANCIAL TIME SERIES, LONG RANGE DEPENDENCE AND THE GARCH MODEL

    E-Print Network [OSTI]

    Mikosch, Thomas

    CHANGE OF STRUCTURE IN FINANCIAL TIME SERIES, LONG RANGE DEPENDENCE AND THE GARCH MODEL THOMAS having as limit a Gaussian #12;eld. In the case of GARCH(p; q) processes a statistic closely related limit theorem for this statistic under the hypothesis of a GARCH(p; q) sequence with a #12;nite 4th

  7. Volatility Forecasts in Financial Time Series with HMM-GARCH Models

    E-Print Network [OSTI]

    Chen, Yiling

    Volatility Forecasts in Financial Time Series with HMM-GARCH Models Xiong-Fei Zhuang and Lai {xfzhuang,lwchan}@cse.cuhk.edu.hk Abstract. Nowadays many researchers use GARCH models to generate of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH

  8. SEAWIFS VALIDATION AT THE CARIBBEAN TIME SERIES STATION (CATS) Jess Lee-Borges* and Roy Armstrong

    E-Print Network [OSTI]

    Gilbes, Fernando

    SEAWIFS VALIDATION AT THE CARIBBEAN TIME SERIES STATION (CATS) Jesús Lee-Borges* and Roy Armstrong. This is of particular importance to areas such as the Eastern Caribbean which has traditionally been viewed the dynamic nature of the northeastern Caribbean, underscoring the significant effect of periodic intrusions

  9. Two-Sample Testing in High Dimension and a Smooth Block Bootstrap for Time Series

    E-Print Network [OSTI]

    Gregory, Karl Bruce

    2014-06-12T23:59:59.000Z

    This document contains three sections. The first two present new methods for two-sample testing where there are many variables of interest and the third presents a new methodology for time series bootstrapping. In the first section we develop a...

  10. Directed Monitoring Using Cuscore Charts for Seasonal Time Series Harriet Black Nembhard*

    E-Print Network [OSTI]

    Nembhard, Harriet Black

    a special cause in a process, statistical process control (SPC) charts are traditionally used. If the data1 Directed Monitoring Using Cuscore Charts for Seasonal Time Series Harriet Black Nembhard used statistical process control charts to detect special causes are Shewhart and Cusum charts. However

  11. A new measure of phase synchronization for a pair of time series and seizure focus localization

    E-Print Network [OSTI]

    Kaushik Majumdar

    2006-12-22T23:59:59.000Z

    Defining and measuring phase synchronization in a pair of nonlinear time series are highly nontrivial. This can be done with the help of Fourier transform, when it exists, for a pair of stored (hence stationary) signals. In a time series instantaneous phase is often defined with the help of Hilbert transform. In this paper phase of a time series has been defined with the help of Fourier transform. This gives rise to a deterministic method to detect phase synchronization in its most general form between a pair of time series. Since this is a stricter method than the statistical methods based on instantaneous phase, this can be used for lateralization and source localization of epileptic seizures with greater accuracy. Based on this method a novel measure of phase synchronization, called syn function, has been defined, which is capable of quantifying neural phase synchronization and asynchronization as important parameters of epileptic seizure dynamics. It has been shown that such a strict measure of phase synchronization has potential application in seizure focus localization from scalp electroencephalogram (EEG) data, without any knowledge of electrical conductivity of the head.

  12. Utility based Data Mining for Time Series Analysis -Cost-sensitive Learning for Neural Network Predictors

    E-Print Network [OSTI]

    Weiss, Gary

    Utility based Data Mining for Time Series Analysis - Cost-sensitive Learning for Neural Network@bis-lab.com ABSTRACT In corporate data mining applications, cost-sensitive learning is firmly established Mining General Terms Algorithms, Management, Economics Keywords Data Mining, cost-sensitive learning

  13. Time Series Measurements of Temperature, Current Velocity, and Sediment Resuspension in Saginaw Bay

    E-Print Network [OSTI]

    Time Series Measurements of Temperature, Current Velocity, and Sediment Resuspension in Saginaw Bay and verification. These measurements will be made as part of this project. Measurements of sediment resuspension sediment resuspension in the bay during the spring. Measurements of sediment resuspension are important

  14. Bispectral-Based Methods for Clustering Time Series Jane L. Harvill

    E-Print Network [OSTI]

    Ravishanker, Nalini

    the ratios. As an example, we apply the method to a set of time series of intensities of gamma-ray bursts, some of which exhibit nonlinear behavior; this enables us to identify gamma-ray bursts that may. As an example, we apply the bispectral-based clustering technique to a set of gamma-ray burst (GRB) intensity

  15. Time Series Analysis with R A. Ian McLeod, Hao Yu, Esam Mahdi

    E-Print Network [OSTI]

    McLeod, Ian

    it is built on a solid foundation of core statistical and numerical algorithms. The R programming languageTime Series Analysis with R A. Ian McLeod, Hao Yu, Esam Mahdi Department of Statistical out some other key features of this quantitative programming environment (QPE). R is an open source

  16. BN-97-4-4 (RP-875) The Radiant Time Series Cooling

    E-Print Network [OSTI]

    of the proceduresare described in chapters 2 and 10 of the current ASHRAECool#zg and Heating LoadCalculation ManualBN-97-4-4 (RP-875) The Radiant Time Series Cooling Load Calculation Procedure Jeffrey D. Spitler calculations, derived from the heat balancemethod.It effectively replacesall other simpli- fied (non-heat

  17. A Novel Approach to the Analysis of Nonlinear Time Series with Applications to Financial Data

    E-Print Network [OSTI]

    Lee, Jun Bum

    2012-07-16T23:59:59.000Z

    LIST OF FIGURES : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : ix CHAPTER I INTRODUCTION : : : : : : : : : : : : : : : : : : : : : : : : : : 1 II THE QUANTILE SPECTRAL DENSITY AND COMPAR- ISON BASED TESTS FOR NONLINEAR TIME SERIES... : : : 5 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. The quantile spectral density and the test statistic . . . . . 7 3. Sampling properties . . . . . . . . . . . . . . . . . . . . . . 18 4. Testing for equality of serial...

  18. Ultrasound radio-frequency time series for finding malignant breast lesions

    E-Print Network [OSTI]

    de Freitas, Nando

    -based solutions for breast lesion characterization to reduce the patient recall rate after mammography screening. In this work, ultrasound radio frequency time series analysis is performed for sepa- rating benign framework can help in differentiating malignant from benign breast lesions. 1 Introduction In the United

  19. Detecting and interpreting distortions in hierarchical organization of complex time series

    E-Print Network [OSTI]

    Dro?d?, Stanis?aw

    2015-01-01T23:59:59.000Z

    Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using the selected time series representing such diverse phenomena like price changes and inter-transaction times in the financial markets, sentence length variability in the narrative texts, Missouri River discharge and Sunspot Number variability as examples, we show that the resulting singularity spectra appear strongly asymmetric, more often left-sided but in some cases also right-sided. We present a unified view on the origin of such effects and indicate that they may be crucially informative for identifying composition of the time series. One particularly intriguing case of this later kind of asymmetry is detected in the daily reported Sunspot Number variability. This signals that either the commonly used famous Wolf formula distorts the real d...

  20. Exploration of period-doubling cascade route to chaos with complex network based time series construction

    E-Print Network [OSTI]

    Ruoxi Xiang; Michael Small

    2014-06-18T23:59:59.000Z

    In this work, the topologies of networks constructed from time series from an underlying system undergo a period doubling cascade have been explored by means of the prevalence of different motifs using an efficient computational motif detection algorithm. By doing this we adopt a refinement based on the $k$ nearest neighbor recurrence-based network has been proposed. We demonstrate that the refinement of network construction together with the study of prevalence of different motifs allows a full explosion of the evolving period doubling cascade route to chaos in both discrete and continuous dynamical systems. Further, this links the phase space time series topologies to the corresponding network topologies, and thus helps to understand the empirical "superfamily" phenomenon, as shown by Xu.

  1. Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

    E-Print Network [OSTI]

    Chattopadhyay, Goutami; 10.1140/epjp/i2012-12043-9

    2012-01-01T23:59:59.000Z

    This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

  2. Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

    E-Print Network [OSTI]

    Goutami Chattopadhyay; Surajit Chattopadhyay

    2012-04-18T23:59:59.000Z

    This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

  3. Time series analysis of ionization waves in dc neon glow discharge

    SciTech Connect (OSTI)

    Hassouba, M. A.; Al-Naggar, H. I.; Al-Naggar, N. M.; Wilke, C. [Department of Physics, Faculty of Science, Benha University (Egypt); Institute of Physics, E. M. A. University, Domstrasse 10a, 17489 Greifswald (Germany)

    2006-07-15T23:59:59.000Z

    The dynamics of dc neon glow discharge is examined by calculating a Lyapunov exponent spectrum (LES) and correlation dimension (D{sub corr}) from experimental time series. The embedding theory is used to reconstruct an attractor with the delay coordinate method. The analysis refers to periodic, chaotic, and quasi-periodic attractors. The results obtained are confirmed by a comparison with other methods of time series analysis such as the Fourier power spectrum and autocorrelation function. The main object of the present work is the positive column of a dc neon glow discharge. The positive column is an excellent model for the study of a non-linearity plasma system because it is nonisothermal plasma far from equilibrium.

  4. Time series analysis, 2013, PC 8 | ARCH and GARCH processes 9 8 ARCH and GARCH processes

    E-Print Network [OSTI]

    Gaïffas, Stéphane

    Time series analysis, 2013, PC 8 | ARCH and GARCH processes 9 8 ARCH and GARCH processes A GARCH(q) process. Exercise 8.2 (Computation of the kurtosis of a conditionally Gaussian GARCH(1, 1) pro- cess that = 3 + 3 Var(E[X2 t | Gt 1]) (E[X2 t ])2 . 3. For a GARCH(1,1) process, with p = q = 1 and a, b = b1, c

  5. Interactive Poster: 3D Axes-Based Visualizations for Time Series Data Christian Tominski James Abello Heidrun Schumann

    E-Print Network [OSTI]

    Tominski, Christian

    that can be used to explore and analyze multivariate time series data. We propose different types of drawings. 1 INTRODUCTION The analysis of time series data is a fundamental task addressed by information of multivariate data is not a new topic to information visualization researchers. A variety of approaches have

  6. A test for second order stationarity of a time series based on the Discrete Fourier Transform -Technical report

    E-Print Network [OSTI]

    Subba Rao, Suhasini

    A test for second order stationarity of a time series based on the Discrete Fourier Transform stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing stationarity of the time series. It is shown that under the null of stationarity, the test statistic has

  7. Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda Atlantic

    E-Print Network [OSTI]

    Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda TOPEX/Poseidon­ERS-1/2) are used to characterize, statistically, the mesoscale variability about the U to better understand the contribution of mesoscale eddies to the time series record and the model- data

  8. Time Series Technical Analysis via new Fast Estimation Methods: A Preliminary Study in Mathematical Finance

    E-Print Network [OSTI]

    Fliess, Michel

    2008-01-01T23:59:59.000Z

    New fast estimation methods stemming from control theory lead to a fresh look at time series, which bears some resemblance to "technical analysis". The results are applied to a typical object of financial engineering, namely the forecast of foreign exchange rates, via a "model-free" setting, i.e., via repeated identifications of low order linear difference equations on sliding short time windows. Several convincing computer simulations, including the prediction of the position and of the volatility with respect to the forecasted trendline, are provided. $\\mathcal{Z}$-transform and differential algebra are the main mathematical tools.

  9. Hurst exponent of very long birth time series in XX century Romania. Social and religious aspects

    E-Print Network [OSTI]

    Rotundo, G; Herteliu, C; Ileanu, B

    2015-01-01T23:59:59.000Z

    The Hurst exponent of very long birth time series in Romania has been extracted from official daily records, i.e. over 97 years between 1905 and 2001 included. The series result from distinguishing between families located in urban (U) or rural (R) areas, and belonging (Ox) or not (NOx) to the orthodox religion. Four time series combining both criteria, (U,R) and (Ox, NOx), are also examined. A statistical information is given on these sub-populations measuring their XX-th century state as a snapshot. However, the main goal is to investigate whether the "daily" production of babies is purely noisy or is fluctuating according to some non trivial fractional Brownian motion, - in the four types of populations, characterized by either their habitat or their religious attitude, yet living within the same political regime. One of the goals was also to find whether combined criteria implied a different behavior. Moreover, we wish to observe whether some seasonal periodicity exists. The detrended fluctuation analysis...

  10. Transportation Energy Futures Series: Vehicle Technology Deployment Pathways: An Examination of Timing and Investment Constraints

    SciTech Connect (OSTI)

    Plotkin, S.; Stephens, T.; McManus, W.

    2013-03-01T23:59:59.000Z

    Scenarios of new vehicle technology deployment serve various purposes; some will seek to establish plausibility. This report proposes two reality checks for scenarios: (1) implications of manufacturing constraints on timing of vehicle deployment and (2) investment decisions required to bring new vehicle technologies to market. An estimated timeline of 12 to more than 22 years from initial market introduction to saturation is supported by historical examples and based on the product development process. Researchers also consider the series of investment decisions to develop and build the vehicles and their associated fueling infrastructure. A proposed decision tree analysis structure could be used to systematically examine investors' decisions and the potential outcomes, including consideration of cash flow and return on investment. This method requires data or assumptions about capital cost, variable cost, revenue, timing, and probability of success/failure, and would result in a detailed consideration of the value proposition of large investments and long lead times. This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for abating GHGs and reducing petroleum dependence related to transportation.

  11. Learning Dynamic Systems From Time-Series Data - An Application to Gene Regulatory Networks

    E-Print Network [OSTI]

    Timoteo, Ivo J. P. M.; Holden, Sean B.

    2015-01-01T23:59:59.000Z

    the second half of the time-series data provided; that is, from the point when the pertur- bation is lifted, as we do not know the exact nature of the perturbation. The DREAM4 Challenge evaluated performance using the p-values for the area under the ROC curve... ., and Druzdzel, M. (2010). Learn- ing why things change: The difference-based causal- ity learner. In Proceedings of the Twenty-Sixth An- nual Conference on Uncertainty in Artificial Intelli- gence (UAI). Yip, K., Alexander, R., Yan, K., and Gerstein, M. (2010...

  12. Analysis and synthesis of the variability of irradiance and PV power time series with the wavelet transform

    SciTech Connect (OSTI)

    Perpinan, O. [Electrical Engineering Department, EUITI-UPM, Ronda de Valencia 3, 28012 Madrid (Spain); Lorenzo, E. [Instituto de Energia Solar, UPM, Ciudad Universitaria s/n, 28040 Madrid (Spain)

    2011-01-15T23:59:59.000Z

    The irradiance fluctuations and the subsequent variability of the power output of a PV system are analysed with some mathematical tools based on the wavelet transform. It can be shown that the irradiance and power time series are nonstationary process whose behaviour resembles that of a long memory process. Besides, the long memory spectral exponent {alpha} is a useful indicator of the fluctuation level of a irradiance time series. On the other side, a time series of global irradiance on the horizontal plane can be simulated by means of the wavestrapping technique on the clearness index and the fluctuation behaviour of this simulated time series correctly resembles the original series. Moreover, a time series of global irradiance on the inclined plane can be simulated with the wavestrapping procedure applied over a signal previously detrended by a partial reconstruction with a wavelet multiresolution analysis, and, once again, the fluctuation behaviour of this simulated time series is correct. This procedure is a suitable tool for the simulation of irradiance incident over a group of distant PV plants. Finally, a wavelet variance analysis and the long memory spectral exponent show that a PV plant behaves as a low-pass filter. (author)

  13. Time series modeling and large scale global solar radiation forecasting from geostationary satellites data

    E-Print Network [OSTI]

    Voyant, Cyril; Muselli, Marc; Paoli, Christophe; Nivet, Marie Laure

    2014-01-01T23:59:59.000Z

    When a territory is poorly instrumented, geostationary satellites data can be useful to predict global solar radiation. In this paper, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour. The results presented in this paper relate to a particular territory, the Corsica Island, but as data used are available for the entire surface of the globe, our method can be easily exploited to another place. Indeed 2-D hourly time series are extracted from the HelioClim-3 surface solar irradiation database treated by the Heliosat-2 model. Each point of the map have been used as training data and inputs of artificial neural networks (ANN) and as inputs for two persistence models (scaled or not). Comparisons between these models and clear sky estimations were proceeded to evaluate the performances. We found a normalized root mean square error (nRMSE) close to 16.5% for the two best predictors (scaled persistence and ANN) equivalent to 35-45% related to ground measurements. F...

  14. Multi-horizon solar radiation forecasting for Mediterranean locations using time series models

    E-Print Network [OSTI]

    Voyant, Cyril; Muselli, Marc; Nivet, Marie Laure

    2013-01-01T23:59:59.000Z

    Considering the grid manager's point of view, needs in terms of prediction of intermittent energy like the photovoltaic resource can be distinguished according to the considered horizon: following days (d+1, d+2 and d+3), next day by hourly step (h+24), next hour (h+1) and next few minutes (m+5 e.g.). Through this work, we have identified methodologies using time series models for the prediction horizon of global radiation and photovoltaic power. What we present here is a comparison of different predictors developed and tested to propose a hierarchy. For horizons d+1 and h+1, without advanced ad hoc time series pre-processing (stationarity) we find it is not easy to differentiate between autoregressive moving average (ARMA) and multilayer perceptron (MLP). However we observed that using exogenous variables improves significantly the results for MLP . We have shown that the MLP were more adapted for horizons h+24 and m+5. In summary, our results are complementary and improve the existing prediction techniques ...

  15. Time Series Analysis Methods Applied to the Super-Kamiokande I Data

    E-Print Network [OSTI]

    Gioacchino Ranucci

    2005-05-25T23:59:59.000Z

    The need to unravel modulations hidden in noisy time series of experimental data is a well known problem, traditionally attacked through a variety of methods, among which a popular tool is the so called Lomb-Scargle periodogram. Recently, for a class of problems in the solar neutrino field, it has been proposed an alternative maximum likelihood based approach, intended to overcome some intrinsic limitations affecting the Lomb-Scargle implementation. This work is focused to highlight the features of the likelihood methodology, introducing in particular an analytical approach to assess the quantitative significance of the potential modulation signals. As an example, the proposed method is applied to the time series of the measured values of the 8B neutrino flux released by the Super-Kamiokande collaboration, and the results compared with those of previous analysis performed on the same data sets. It is also examined in detail the comparison between the Lomb-Scargle and the likelihood methods, giving in the appendix the complete demonstration of their close relationship.

  16. Studying accreting black holes and neutron stars with time series: beyond the power spectrum

    E-Print Network [OSTI]

    S. Vaughan; P. Uttley

    2008-02-04T23:59:59.000Z

    The fluctuating brightness of cosmic X-ray sources, particularly accreting black holes and neutron star systems, has enabled enormous progress in understanding the physics of turbulent accretion flows, the behaviour of matter on the surfaces of neutron stars and improving the evidence for black holes. Most of this progress has been made by analysing and modelling time series data in terms of their power and cross spectra, as will be discussed in other articles in this volume. Recently, attempts have been made to make use of other aspects of the data, by testing for non-linearity, non-Gaussianity, time asymmetry and by examination of higher order Fourier spectra. These projects, which have been made possible by the vast increase in data quality and quantity over the past decade, are the subject of this article.

  17. Separation of Stochastic and Deterministic Information from Seismological Time Series with Nonlinear Dynamics and Maximum Entropy Methods

    SciTech Connect (OSTI)

    Gutierrez, Rafael M.; Useche, Gina M.; Buitrago, Elias [Centro de Investigaciones, Universidad Antonio Narino, Carrera 3 Este No. 47A--15 Bogota (Colombia)

    2007-11-13T23:59:59.000Z

    We present a procedure developed to detect stochastic and deterministic information contained in empirical time series, useful to characterize and make models of different aspects of complex phenomena represented by such data. This procedure is applied to a seismological time series to obtain new information to study and understand geological phenomena. We use concepts and methods from nonlinear dynamics and maximum entropy. The mentioned method allows an optimal analysis of the available information.

  18. Spectral fluctuations of billiards with mixed dynamics: from time series to superstatistics

    E-Print Network [OSTI]

    A. Y. Abul-Magd; B. Dietz; T. Friedrich; A. Richter

    2008-03-22T23:59:59.000Z

    A statistical analysis of the eigenfrequencies of two sets of superconducting microwave billiards, one with mushroom-like shape and the other from the familiy of the Limacon billiards, is presented. These billiards have mixed regular-chaotic dynamics but different structures in their classical phase spaces. The spectrum of each billiard is represented as a time series where the level order plays the role of time. Two most important findings follow from the time-series analysis. First, the spectra can be characterized by two distinct relaxation lengths. This is a prerequisite for the validity of the superstatistical approach which is based on the folding of two distribution functions. Second, the shape of the resulting probability density function of the so-called superstatistical parameter is reasonably approximated by an inverse chi-square distribution. This distribution is used to compute nearest-neighbor spacing distributions and compare them with those of the resonance frequencies of billiards with mixed dynamics within the framework of superstatistics. The obtained spacing distribution is found to present a good description of the experimental ones and is of the same or even better quality as a number of other spacing distributions, including the one from Berry and Robnik. However, in contrast to other approaches towards a theoretical description of spectral properties of systems with mixed dynamics, superstatistics also provides a description of properties of the eigenfunctions. Indeed, the inverse chi-square parameter distribution is found suitable for the analysis of experimental resonance strengths in the Limacon billiards within the framework of superstatistics.

  19. Precursory signatures of protein folding/unfolding: From time series correlation analysis to atomistic mechanisms

    SciTech Connect (OSTI)

    Hsu, P. J.; Lai, S. K., E-mail: sklai@coll.phy.ncu.edu.tw [Complex Liquids Laboratory, Department of Physics, National Central University, Chungli 320 Taiwan (China); Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan (China); Cheong, S. A. [Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371 (Singapore)

    2014-05-28T23:59:59.000Z

    Folded conformations of proteins in thermodynamically stable states have long lifetimes. Before it folds into a stable conformation, or after unfolding from a stable conformation, the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1) ultrafast shape recognition, (2) time series segmentation, and (3) time series correlation analysis. The first procedure measures the differences between statistical distance distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, we employ the third technique to exploit spatial fingerprints of the stable conformations; this procedure is to map out the sequences of changes preceding the actual folding and unfolding events, since strongly correlated atoms in different conformations are different due to bond and steric constraints. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational and structural changes that are associated with rate-limiting precursors that translate into brief segments. Guided by these technical procedures, we choose a model system, a fragment of the protein transthyretin, for identifying in this system not only the precursory signatures of transitions associated with ? helix and ? hairpin, but also the important role played by weaker correlations in such protein folding dynamics.

  20. Time series of high resolution spectra of SN 2014J observed with the TIGRE telescope

    E-Print Network [OSTI]

    Jack, D; Schroder, K -P; Schmitt, J H M M; Hempelmann, A; Gonzalez-Perez, J N; Trinidad, M A; Rauw, G; Sixto, J M Cabrera

    2015-01-01T23:59:59.000Z

    We present a time series of high resolution spectra of the Type Ia supernova 2014J, which exploded in the nearby galaxy M82. The spectra were obtained with the HEROS echelle spectrograph installed at the 1.2 m TIGRE telescope. We present a series of 33 spectra with a resolution of R = 20, 000, which covers the important bright phases in the evolution of SN 2014J during the period from January 24 to April 1 of 2014. The spectral evolution of SN 2014J is derived empirically. The expansion velocities of the Si II P-Cygni features were measured and show the expected decreasing behaviour, beginning with a high velocity of 14,000 km/s on January 24. The Ca II infrared triplet feature shows a high velocity component with expansion velocities of > 20, 000 km/s during the early evolution apart from the normal component showing similar velocities as Si II. Further broad P-Cygni profiles are exhibited by the principal lines of Ca II, Mg II and Fe II. The TIGRE SN 2014J spectra also resolve several very sharp Na I D doub...

  1. WAVELETS WITH RIDGES: A HIGH-RESOLUTION REPRESENTATION OF CATACLYSMIC VARIABLE TIME SERIES

    SciTech Connect (OSTI)

    Blackman, Claire, E-mail: claire.blackman@rhul.ac.u [Department of Economics, Royal Holloway, University of London, Egham, Surrey TW20 0EX (United Kingdom)

    2010-11-15T23:59:59.000Z

    Quasi-periodic oscillations (QPO) and dwarf nova oscillations (DNOs) occur in dwarf novae and nova-like variables during outburst and occasionally during quiescence, and have analogs in high-mass X-ray binaries and black-hole candidates. The frequent low coherence of quasi-period oscillations and DNOs can make detection with standard time-series tools such as periodograms problematic. This paper develops tools to analyze quasi-periodic brightness oscillations. We review the use of time-frequency representations (TFRs) in the astronomical literature, and show that representations such as the Choi-Williams distribution and Zhao-Atlas-Marks representation, which are best suited to high signal-to-noise data, cannot be assumed a priori to be the best techniques for our data, which have a much higher noise level and lower coherence. This leads us to a detailed analysis of the time-frequency resolution and statistical properties of six TFRs. We conclude that the wavelet scalogram, with the addition of wavelet ridges and maxima points, is the most effective TFR for analyzing quasi-periodicities in low signal-to-noise data, as it has high time-frequency resolution, and is a minimum variance estimator. We use the wavelet ridges method to re-analyze archival data from VW Hyi, and find 62 new QPOs and 7 new long-period DNOs. Relative to previous analyses, our method substantially improves the detection rate for QPOs.

  2. Multiple Alignment of Continuous Time Series Jennifer Listgarten y , Radford M. Neal y , Sam T. Roweis y and Andrew Emili z

    E-Print Network [OSTI]

    Roweis, Sam

    Multiple Alignment of Continuous Time Series Jennifer Listgarten y , Radford M. Neal y , Sam T of continuous­valued time series from a stochastic process often contain systematic variations in rate time series generated by a noisy, stochastic process, large sys­ tematic sources of variability

  3. Simulation of wind-speed time series for wind-energy conversion analysis.

    SciTech Connect (OSTI)

    Corotis, R.B.

    1982-06-01T23:59:59.000Z

    In order to investigate operating characteristics of a wind energy conversion system it is often desirable to have a sequential record of wind speeds. Sometimes a long enough actual data record is not available at the time an analysis is needed. This may be the case if, e.g., data are recorded three times a day at a candidate wind turbine site, and then the hourly performance of generated power is desired. In such cases it is often possible to use statistical characteristics of the wind speed data to calibrate a stochastic model and then generate a simulated wind speed time series. Any length of record may be simulated by this method, and desired system characteristics may be studied. A simple wind speed simulation model, WEISIM, is developed based on the Weibull probability distribution for wind speeds with a correction based on the lag-one autocorrelation value. The model can simulate at rates from one a second to one an hour, and wind speeds can represent short-term averages (e.g., 1-sec averages) or longer-term averages (e.g., 1-min or 1 hr averages). The validity of the model is verified with PNL data for both histogram characteristics and persistance characteristics.

  4. Iterative prediction of chaotic time series using a recurrent neural network. Quarterly progress report, January 1, 1995--March 31, 1995

    SciTech Connect (OSTI)

    Bodruzzaman, M.; Essawy, M.A.

    1996-03-31T23:59:59.000Z

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neural network used should be capable of modeling the highly non-linear behavior and the multi- attractor nature of such systems. In this paper we use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

  5. Binary versus non-binary information in real time series: empirical results and maximum-entropy matrix models

    E-Print Network [OSTI]

    Almog, Assaf

    2014-01-01T23:59:59.000Z

    The dynamics of complex systems, from financial markets to the brain, can be monitored in terms of time series of activity of their fundamental elements (such as stocks or neurons respectively). While the main focus of time series analysis is on the magnitude of temporal increments, a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. In this paper we provide further evidence of this by showing strong nonlinear relationships between binary and non-binary properties of financial time series. We then introduce an information-theoretic approach to the analysis of the binary signature of single and multiple time series. Through the definition of maximum-entropy ensembles of binary matrices, we quantify the information encoded into the simplest binary properties of real time series and identify the most informative property given a set of measurements. Our formalism is able to replicate the observed binary/non-binary relations very well, and to mathematically...

  6. Time-series analysis of participation in nonresident hunting: the effects of license cost and quantitative fluctuations in supply.

    E-Print Network [OSTI]

    Mazzaccaro, Anthony Peter

    1974-01-01T23:59:59.000Z

    TIME -SERIES ANAI, YSIS OI' PARTICIPATION IN NQiIRESI DEN I. ' HUNTING: Tl-;E EFFECTS OI LICENSE COST ANI3 QUANTITATIVE I LUC fUWTIONS IN SVPPI. Y A lil*sis by ANTHONY PETER MAZZACCARO Subrnittc. d to the Gracluate College of Teresa ARM Unic... AND QUANTITATIVE Fl UCTUATIONS IN SUPPLY A Thesis by ANTHONY PETER IvlAZZACCARO Approved as to style and content: (Chairman of Conrrnittee) ead of Department) ( ivl e rnb e g ~. , 8! (Member) +~eg ~+ ABSTRACT Time-Series Analysis of Participation...

  7. Reverberation mapping of active galactic nuclei : The SOLA method for time-series inversion

    E-Print Network [OSTI]

    Frank P. Pijpers; Ignaz Wanders

    1994-06-27T23:59:59.000Z

    In this paper a new method is presented to find the transfer function of the broad-line region in active galactic nuclei. The subtractive optimally localized averages (SOLA) method is a modified version of the Backus-Gilbert method and is presented as an alternative to the more often used maximum-entropy method. The SOLA method has been developed for use in helioseismology. It has been applied to the solar oscillation frequency splitting data currently available to deduce the internal rotation rate of the sun. The original SOLA method is reformulated in the present paper to cope with the slightly different problem of inverting time series. We use simulations to test the viability of the method and apply the SOLA method to the real data of the Seyfert-1 galaxy NGC 5548. We investigate the effects of measurement errors and how the resolution of the TF critically depends upon both the sampling rate and the photometric accuracy of the data. A uuencoded compressed postscript file of the paper which includes the figures is available by anonymous ftp at ftp://solaris.astro.uu.se/pub/articles/atmos/frank/PijWan.uue

  8. PRECISE HIGH-CADENCE TIME SERIES OBSERVATIONS OF FIVE VARIABLE YOUNG STARS IN AURIGA WITH MOST

    SciTech Connect (OSTI)

    Cody, Ann Marie; Tayar, Jamie; Hillenbrand, Lynne A. [Department of Astrophysics, California Institute of Technology, MC 249-17, Pasadena, CA 91125 (United States); Matthews, Jaymie M. [Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, British Columbia V6T 1Z1 (Canada); Kallinger, Thomas, E-mail: amc@ipac.caltech.edu [Institut fuer Astronomie, Universitaet Wien, Tuerkenschanzstrasse 17, A-1180 Vienna (Austria)

    2013-03-15T23:59:59.000Z

    To explore young star variability on a large range of timescales, we have used the MOST satellite to obtain 24 days of continuous, sub-minute cadence, high-precision optical photometry on a field of classical and weak-lined T Tauri stars (TTSs) in the Taurus-Auriga star formation complex. Observations of AB Aurigae, SU Aurigae, V396 Aurigae, V397 Aurigae, and HD 31305 reveal brightness fluctuations at the 1%-10% level on timescales of hours to weeks. We have further assessed the variability properties with Fourier, wavelet, and autocorrelation techniques, identifying one significant period per star. We present spot models in an attempt to fit the periodicities, but find that we cannot fully account for the observed variability. Rather, all stars exhibit a mixture of periodic and aperiodic behavior, with the latter dominating stochastically on timescales less than several days. After removal of the main periodicity, periodograms for each light curve display power-law trends consistent with those seen for other young accreting stars. Several of our targets exhibited unusual variability patterns not anticipated by prior studies, and we propose that this behavior originates with the circumstellar disks. The MOST observations underscore the need for investigation of TTS light variations on a wide range of timescales in order to elucidate the physical processes responsible; we provide guidelines for future time series observations.

  9. A Scientific Data Processing Framework for Time Series NetCDF Data

    SciTech Connect (OSTI)

    Gaustad, Krista L.; Shippert, Timothy R.; Ermold, Brian D.; Beus, Sherman J.; Daily, Jeffrey A.; Borsholm, Atle; Fox, Kevin M.

    2014-10-01T23:59:59.000Z

    ARM Data Integrator (ADI) is a framework to streamline the development of scientific algorithms that analyze time-series NetCDF data, and to improve the content and consistency of the output data products produced by these algorithms. ADI achieves these goals by automating the process of retrieving and preparing data for analysis, supporting the definition of output data products through a graphical interface, and providing a modular, flexible software development architecture. The input data, preprocessing, and output data specifications are defined through a graphical interface and stored in a database. ADI also includes a workflow for data integration, a library of software modules to support the workflow, and a source code generator that produces C, IDL and Python templates. Data preparation support includes automated retrieval of data from input files, merging the retrieved data into appropriately sized chunks, and transformation of the data onto a common coordinate system grid. Through the graphical interface, users can view the details of both their data products and those in the ARM catalog. The variable and attribute definitions of the existing data products can be used to build new output data products. In addition, the rules that make up the ARM archive’s data standards are laid on top of the view of the new data product providing the user with a visual cue indicating where their output violates an archive standard. The necessary configurations are stored in a database that is accessed by the ADI libraries. This paper discusses the ADI framework, its supporting components, and how ADI can significantly decrease the time and cost of implementing scientific algorithms while improving the ability of scientists to disseminate their results.

  10. Finding Statistics & Data at Queen's Sept/08 STATISTICS Facts & figures in tables, charts, time series, graphs, etc.

    E-Print Network [OSTI]

    Abolmaesumi, Purang

    Finding Statistics & Data at Queen's Sept/08 STATISTICS Facts & figures in tables, charts, time series, graphs, etc. 1. Statistics Canada www.statcan.ca English use the search box... REMEMBER: Don't Pay Contact madgic@queensu.ca to get statistics for free if faced with a fee! 2. Social

  11. An Indexing Scheme for Fast Similarity Search in Large Time Series Databases Eamonn J. Keogh and Michael J. Pazzani

    E-Print Network [OSTI]

    Pazzani, Michael J.

    An Indexing Scheme for Fast Similarity Search in Large Time Series Databases Eamonn J. Keogh, California 92697 USA {eamonn,pazzani}@ics.uci.edu Abstract We address the problem of similarity search similar element of the bin. This bound allows us to search the bins in best first order, and to prune some

  12. High-frequency precipitation and stream water quality time series from Plynlimon, Wales: an openly accessible data

    E-Print Network [OSTI]

    Kirchner, James W.

    High-frequency precipitation and stream water quality time series from Plynlimon, Wales: an openly Colin Vincent,6 Kathryn Lehto,6 Simon Grant,2 Jeremy Williams,7 Margaret Neal,1 Heather Wickham,1 Sarah-element high- frequency water quality data set that is openly accessible to the research community. The data

  13. Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis

    E-Print Network [OSTI]

    Andresen, Gorm Bruun; Greiner, Martin

    2014-01-01T23:59:59.000Z

    We present a new global high-resolution renewable energy atlas (REatlas) that can be used to calculate customised hourly time series of wind and solar PV power generation. In this paper, the atlas is applied to produce 32-year-long hourly model wind power time series for Denmark for each historical and future year between 1980 and 2035. These are calibrated and validated against real production data from the period 2000 to 2010. The high number of years allows us to discuss how the characteristics of Danish wind power generation varies between individual weather years. As an example, the annual energy production is found to vary by $\\pm10\\%$ from the average. Furthermore, we show how the production pattern change as small onshore turbines are gradually replaced by large onshore and offshore turbines. In most energy system analysis tools, fixed hourly time series of wind power generation are used to model future power systems with high penetrations of wind energy. Here, we compare the wind power time series fo...

  14. Hierarchical Bivariate Time Series Models: A Combined Analysis of the Effects of Particulate Matter on Morbidity and

    E-Print Network [OSTI]

    Dominici, Francesca

    for 10 metropolitan areas in the United States from 1986 to 1993. We postulate that these time series relative rates of mortality and morbidity associated with exposure to PM10 within each location. The sample covariance matrix of the estimated log relative rates is obtained using a novel generalized estimating

  15. Micro-phytoplankton variability at the equatorial Pacific (140W?) during the JGOFS EQPAC Time Series Studies 1992 

    E-Print Network [OSTI]

    Iriarte, Jose Luis

    1994-01-01T23:59:59.000Z

    Micro-phytoplankton (>20 gm cell size) was sampled in the upper 200 m of the water column at the Pacific equator, 140'W, during two JGOFS EqPac Time Series Studies, in order to determine the changes in the micro-phytoplanlcton ...

  16. A Fuzzy-Convolution Model for Physical Action and Behaviour Pattern Recognition of 3D Time Series

    E-Print Network [OSTI]

    Hu, Huosheng

    A Fuzzy-Convolution Model for Physical Action and Behaviour Pattern Recognition of 3D Time Series-- Pattern Classification, Action Recognition, Fuzzy Classifiers, Signal Convolution. I. INTRODUCTION researchers in pattern recognition on the field of intelligent surveillance. Fuzzy logic has been extensively

  17. Local Load Analysis with Periodic Time Series and Temperature Adjustment Marcelo Espinoza, Bart De Moor Caroline Joye Ronnie Belmans

    E-Print Network [OSTI]

    Time Series, Load Profiles, Temperature Sensitivity, Weather Adjustment 1 Introduction The quantitative, it is required to use indirect techniques to assess the type of demand they face [10, 11] in order to support their long-term investment planning. In this context, categories of residential, business and in- dustrial

  18. Glacier mass balance determination by Remote Sensing in the French Alps: Progress and limitation for time series monitoring

    E-Print Network [OSTI]

    Rabatel, Antoine

    measurements. A recent time series of images from optical and SAR data are selected on 3 outlet glaciers well-scale areas. The limitations are cloudiness for optical data and high slope distortion on SAR images. I resolution, repeat coverage, radiometric calibration and stereo capabilities (automatic generation of DEM

  19. ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA

    SciTech Connect (OSTI)

    Richards, Joseph W.; Starr, Dan L.; Butler, Nathaniel R.; Bloom, Joshua S.; Crellin-Quick, Arien; Higgins, Justin; Kennedy, Rachel; Rischard, Maxime [Astronomy Department, University of California, Berkeley, CA 94720-7450 (United States); Brewer, John M., E-mail: jwrichar@stat.berkeley.edu [Astronomy Department, Yale University, New Haven, CT 06520-8101 (United States)

    2011-05-20T23:59:59.000Z

    With the coming data deluge from synoptic surveys, there is a need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly observed variables based on small numbers of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics (features), detail methods to robustly estimate periodic features, introduce tree-ensemble methods for accurate variable-star classification, and show how to rigorously evaluate a classifier using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% error rate using the random forest (RF) classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. The RF classifier is superior to other methods in terms of accuracy, speed, and relative immunity to irrelevant features; the RF can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which reduces the catastrophic error rate from 8% to 7.8%. Excluding low-amplitude sources, the overall error rate improves to 14%, with a catastrophic error rate of 3.5%.

  20. Likelihood scan of the Super-Kamiokande I time series data

    E-Print Network [OSTI]

    Gioacchino Ranucci

    2006-05-12T23:59:59.000Z

    In this work a detailed spectral analysis of the time series of the 8B solar neutrino flux published by the Super-Kamiokande Collaboration is presented, performed through a likelihood scan approach. Preliminarily a careful review of the analysis methodology is given, showing that the traditional periodicity search via the Lomb-Scargle periodogram is a special case of a more general likelihood based method. Since the data are published together with the relevant asymmetric errors, it is then shown how the likelihood analysis can be performed either with or without a prior error averaging. A key point of this work is the detailed illustration of the mathematical model describing the statistical properties of the estimated spectra obtained in the various cases, which is also validated through extensive Monte Carlo computations; the model includes a calculation for the prediction of the possible alias effects. In the successive investigation of the data, such a model is used to derive objective, mathematical predictions which are quantitatively compared with the features observed in the experimental spectra. This article clearly demonstrates that the handling of the errors is the origin of the discrepancy between published null observations and claimed significant periodicity in the same SK-I data sample. Moreover, the comprehensive likelihood analysis with asymmetric errors developed in this work provides results which cannot exclude the null hypothesis of constant rate, even though some indications stemming from the model at odd with such conclusion point towards the desirability of additional investigations with alternative methods to shed further light on the characteristics of the data.

  1. IGR For GR/M76881/01: Generating Summaries of Time-Series Data (SumTime) Background/Context

    E-Print Network [OSTI]

    Sripada, Yaji

    of numerical time-series data. The modern world is being flooded with such data. For example, a typical gas-turbine worked in three domains: weather forecasts, summaries of gas-turbine sensor data, and summaries of sensor number of input data values; this meant it could not be used in our hospital and gas-turbine domains

  2. A Time Series Analysis of Food Price and Its Input Prices 

    E-Print Network [OSTI]

    Routh, Kari 1988-

    2012-11-27T23:59:59.000Z

    of crude oil, gasoline, corn, and ethanol prices, as well as, the relative foreign exchange rate of the U.S. dollar and producer price indexes for food manufacturing and fuel products on domestic food prices are examined. Because the data series are non...

  3. A Time Series Analysis of Food Price and Its Input Prices

    E-Print Network [OSTI]

    Routh, Kari 1988-

    2012-11-27T23:59:59.000Z

    of crude oil, gasoline, corn, and ethanol prices, as well as, the relative foreign exchange rate of the U.S. dollar and producer price indexes for food manufacturing and fuel products on domestic food prices are examined. Because the data series are non...

  4. Financial time series forecasting with a bio-inspired fuzzy model Jos Luis Aznarte a,

    E-Print Network [OSTI]

    Granada, Universidad de

    Alcalá-Fdez b , Antonio Arauzo-Azofra c , José Manuel Benítez b a Center for Energy and Processes (CEP series, as stock prices or level of indices, is a controversial issue which has been questioned nature, the most salient of which is the well-known ARMA model by Box and Jenkins (1970). However, due

  5. Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series

    E-Print Network [OSTI]

    Ryabko, Boris

    2007-01-01T23:59:59.000Z

    We address the problem of nonparametric estimation of characteristics for stationary and ergodic time series. We consider finite-alphabet time series and real-valued ones and the following four problems: i) estimation of the (limiting) probability (or estimation of the density for real-valued time series), ii) on-line prediction, iii) regression and iv) classification (or so-called problems with side information). We show that so-called archivers (or data compressors) can be used as a tool for solving these problems. In particular, firstly, it is proven that any so-called universal code (or universal data compressor) can be used as a basis for constructing asymptotically optimal methods for the above problems. (By definition, a universal code can "compress" any sequence generated by a stationary and ergodic source asymptotically till the Shannon entropy of the source.) And, secondly, we show experimentally that estimates, which are based on practically used methods of data compression, have a reasonable preci...

  6. An Approach to Generating Summaries of Time Series Data in the Gas Turbine Domain Jin Yu and Jim Hunter and Ehud Reiter and Somayajulu Sripada

    E-Print Network [OSTI]

    Sripada, Yaji

    An Approach to Generating Summaries of Time Series Data in the Gas Turbine Domain Jin Yu and Jim an approach to generating summaries of time series data in the gas turbine domain using AI techniques. Through the production of textual summaries. We extend KBTA framework to the gas turbine domain and propose to generate

  7. Time-series investigation of anomalous thermocouple responses in a liquid-metal-cooled reactor

    SciTech Connect (OSTI)

    Gross, K.C.; Planchon, H.P.; Poloncsik, J.

    1988-03-24T23:59:59.000Z

    A study was undertaken using SAS software to investigate the origin of anomalous temperature measurements recorded by thermocouples (TCs) in an instrumented fuel assembly in a liquid-metal-cooled nuclear reactor. SAS macros that implement univariate and bivariate spectral decomposition techniques were employed to analyze data recorded during a series of experiments conducted at full reactor power. For each experiment, data from physical sensors in the tests assembly were digitized at a sampling rate of 2/s and recorded on magnetic tapes for subsequent interactive processing with CMS SAS. Results from spectral and cross-correlation analyses led to the identification of a flow rate-dependent electromotive force (EMF) phenomenon as the origin of the anomalous TC readings. Knowledge of the physical mechanism responsible for the discrepant TC signals enabled us to device and justify a simple correction factor to be applied to future readings.

  8. Scaling analysis of time series of daily prices from stock markets of transitional economies in the Western Balkans

    E-Print Network [OSTI]

    Savran, Darko; Blesic, Suzana; Miljkovic, Vladimir

    2014-01-01T23:59:59.000Z

    In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our findings: Detrended Fluctuation Analysis (DFA) method, Detrended Moving Average (DMA) method, and Wavelet Transformation (WT) analysis. We have found scaling behavior in all SMI data sets that we have analyzed. The scaling of our SMI series changes from long-range correlated to slightly anti-correlated behavior with the change in growth or maturity of the economy the stock market is embedded in. We also report the presence of effects of potential periodic-like influences on the SMI data that we have analyzed. One such influence is visible in all our SMI series, and appears at a period $T_{p}\\approx 90$ days. We propose that the existence of various periodic-like influences on SMI data may partially...

  9. TIME SERIES ANALYSIS FOR THE CF SOURCE IN SUDBURY NEUTRINO OBSERVATORY

    E-Print Network [OSTI]

    Analysis . . . . . . . . . . . . . . . . . . . . . . . . 4 2 The Theory Leading to the Californium Time . . . . . . . . . . . . . 10 2.1.3 A Typical Event Chronology . . . . . . . . . . . . . . . . . . . 13 2.2 The Survival Function . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 The Survival Function for a Single

  10. Looking for granulation and periodicity imprints in the sunspot time series

    E-Print Network [OSTI]

    Lopes, Ilidio

    2015-01-01T23:59:59.000Z

    The sunspot activity is the end result of the cyclic destruction and regeneration of magnetic fields by the dynamo action. We propose a new method to analyze the daily sunspot areas data recorded since 1874. By computing the power spectral density of daily data series using the Mexican hat wavelet, we found a power spectrum with a well-defined shape, characterized by three features. The first term is the 22 yr solar magnetic cycle, estimated in our work to be of 18.43 yr. The second term is related to the daily volatility of sunspots. This term is most likely produced by the turbulent motions linked to the solar granulation. The last term corresponds to a periodic source associated with the solar magnetic activity, for which the maximum of power spectral density occurs at 22.67 days. This value is part of the 22-27 day periodicity region that shows an above-average intensity in the power spectra. The origin of this 22.67 day periodic process is not clearly identified, and there is a possibility that it can be...

  11. Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

    E-Print Network [OSTI]

    of users along a specific time period. These statistics can then help the energy provider perform various operations such as load balancing and forecasting for potential acquirement. Despite its merits, statistical analyzer to compute global statistics over the set of individual inputs that are protected by some

  12. Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling

    E-Print Network [OSTI]

    Fernandez, Thomas

    Comparative application of artificial neural networks and genetic algorithms for multivariate time of artificial neural networks and genetic algorithms in terms of forecasting and understanding of algal blooms-a, Microcystis, short-term prediction, artificial neural network model, genetic algorithm model, rule sets

  13. Sediment transport time measured with U-Series isotopes: Resultsfrom ODP North Atlantic Drill Site 984

    SciTech Connect (OSTI)

    DePaolo, Donald J.; Maher, Kate; Christensen, John N.; McManus,Jerry

    2006-06-05T23:59:59.000Z

    High precision uranium isotope measurements of marineclastic sediments are used to measure the transport and storage time ofsediment from source to site of deposition. The approach is demonstratedon fine-grained, late Pleistocene deep-sea sediments from Ocean DrillingProgram Site 984A on the Bjorn Drift in the North Atlantic. The sedimentsare siliciclastic with up to 30 percent carbonate, and dated by sigma 18Oof benthic foraminifera. Nd and Sr isotopes indicate that provenance hasoscillated between a proximal source during the last three interglacialperiods volcanic rocks from Iceland and a distal continental sourceduring glacial periods. An unexpected finding is that the 234U/238Uratios of the silicate portion of the sediment, isolated by leaching withhydrochloric acid, are significantly less than the secular equilibriumvalue and show large and systematic variations that are correlated withglacial cycles and sediment provenance. The 234U depletions are inferredto be due to alpha-recoil loss of234Th, and are used to calculate"comminution ages" of the sediment -- the time elapsed between thegeneration of the small (<_ 50 mu-m) sediment grains in the sourceareas by comminution of bedrock, and the time of deposition on theseafloor. Transport times, the difference between comminution ages anddepositional ages, vary from less than 10 ky to about 300 to 400 ky forthe Site 984A sediments. Long transport times may reflect prior storagein soils, on continental shelves, or elsewhere on the seafloor. Transporttime may also be a measure of bottom current strength. During the mostrecent interglacial periods the detritus from distal continental sourcesis diluted with sediment from Iceland that is rapidly transported to thesite of deposition. The comminution age approach could be used to dateQuaternary non-marine sediments, soils, and atmospheric dust, and may beenhanced by concomitant measurement of 226Ra/230Th, 230Th/234U, andcosmogenic nuclides.

  14. A time-series study of the health effects of water-soluble and total-extractable metal content of airborne particulate matter 

    E-Print Network [OSTI]

    Heal, Mathew R; Elton, Robert A; Hibbs, Leon R; Agius, Raymond M; Beverland, Iain J

    2009-01-01T23:59:59.000Z

    -soluble and total-extractable content of 11 trace metals determined in each sample. Time series were analysed using generalised additive Poisson regression models, including adjustment for minimum temperature and loess smoothing of trends. Methods were explored...

  15. Jin Yu, Ehud Reiter, Jim Hunter and Chris Mellish Choosing the content of textual summaries of large time-series data sets

    E-Print Network [OSTI]

    Sripada, Yaji

    generates summaries of sensor data from a gas turbine. Table 1. Part of a sample of time series data describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textual summaries of sensor data from gas turbines. 1 Introduction It is often said in the NLP community

  16. A Bayesian method for characterizing distributed micro-releases: II. inference under model uncertainty with short time-series data.

    SciTech Connect (OSTI)

    Marzouk, Youssef; Fast P. (Lawrence Livermore National Laboratory, Livermore, CA); Kraus, M. (Peterson AFB, CO); Ray, J. P.

    2006-01-01T23:59:59.000Z

    Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern after the anthrax attacks of 2001. The ability to characterize such attacks, i.e., to estimate the number of people infected, the time of infection, and the average dose received, is important when planning a medical response. We address this question of characterization by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To be of relevance to response planning, we limit ourselves to 3-5 days of data. In tests performed with anthrax as the pathogen, we find that these data are usually sufficient, especially if the model of the outbreak used in the inverse problem is an accurate one. In some cases the scarcity of data may initially support outbreak characterizations at odds with the true one, but with sufficient data the correct inferences are recovered; in other words, the inverse problem posed and its solution methodology are consistent. We also explore the effect of model error-situations for which the model used in the inverse problem is only a partially accurate representation of the outbreak; here, the model predictions and the observations differ by more than a random noise. We find that while there is a consistent discrepancy between the inferred and the true characterizations, they are also close enough to be of relevance when planning a response.

  17. Time series association learning

    DOE Patents [OSTI]

    Papcun, George J. (Santa Fe, NM)

    1995-01-01T23:59:59.000Z

    An acoustic input is recognized from inferred articulatory movements output by a learned relationship between training acoustic waveforms and articulatory movements. The inferred movements are compared with template patterns prepared from training movements when the relationship was learned to regenerate an acoustic recognition. In a preferred embodiment, the acoustic articulatory relationships are learned by a neural network. Subsequent input acoustic patterns then generate the inferred articulatory movements for use with the templates. Articulatory movement data may be supplemented with characteristic acoustic information, e.g. relative power and high frequency data, to improve template recognition.

  18. PARAMETER ESTIMATION FROM TIME-SERIES DATA WITH CORRELATED ERRORS: A WAVELET-BASED METHOD AND ITS APPLICATION TO TRANSIT LIGHT CURVES

    SciTech Connect (OSTI)

    Carter, Joshua A.; Winn, Joshua N., E-mail: carterja@mit.ed, E-mail: jwinn@mit.ed [Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)

    2009-10-10T23:59:59.000Z

    We consider the problem of fitting a parametric model to time-series data that are afflicted by correlated noise. The noise is represented by a sum of two stationary Gaussian processes: one that is uncorrelated in time, and another that has a power spectral density varying as 1/f{sup g}amma. We present an accurate and fast [O(N)] algorithm for parameter estimation based on computing the likelihood in a wavelet basis. The method is illustrated and tested using simulated time-series photometry of exoplanetary transits, with particular attention to estimating the mid-transit time. We compare our method to two other methods that have been used in the literature, the time-averaging method and the residual-permutation method. For noise processes that obey our assumptions, the algorithm presented here gives more accurate results for mid-transit times and truer estimates of their uncertainties.

  19. Analysis of MALDI FT-ICR Mass Spectrometry Data: a Time Series Donald A. Barkauskasa, Scott R. Kronewitterb, Carlito B. Lebrillab, and David M. Rockec

    E-Print Network [OSTI]

    Rocke, David M.

    Analysis of MALDI FT-ICR Mass Spectrometry Data: a Time Series Approach Donald A. Barkauskasa/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables

  20. Estimation of a Noise Level Using Coarse-Grained Entropy of Experimental Time Series of Internal Pressure in a Combustion Engine

    E-Print Network [OSTI]

    Grzegorz Litak; Rodolfo Taccani; Krzysztof Urbanowicz; Janusz A. Holyst; Miroslaw Wendeker; Alessandro Giadrossi

    2004-05-22T23:59:59.000Z

    We report our results on non-periodic experimental time series of pressure in a single cylinder spark ignition engine. The experiments were performed for different levels of loading. We estimate the noise level in internal pressure calculating the coarse-grained entropy from variations of maximal pressures in successive cycles. The results show that the dynamics of the combustion is a nonlinear multidimensional process mediated by noise. Our results show that so defined level of noise in internal pressure is not monotonous function of loading.

  1. A case study of an expert mathematics teacher's interactive decision-making system using physiological and behavioral time series data

    E-Print Network [OSTI]

    Jensen, Deborah Larkey

    2005-02-17T23:59:59.000Z

    The purpose of this exploratory case study was to describe an expert teacher?s decision-making system during interactive instruction using teacher self-report information, classroom observation data, and physiological recordings. Timed recordings...

  2. A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential

    SciTech Connect (OSTI)

    Wang, Ruofan; Wang, Jiang; Deng, Bin, E-mail: dengbin@tju.edu.cn; Liu, Chen; Wei, Xile [Department of Electrical and Automation Engineering, Tianjin University, Tianjin (China)] [Department of Electrical and Automation Engineering, Tianjin University, Tianjin (China); Tsang, K. M.; Chan, W. L. [Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon (Hong Kong)] [Department of Electrical Engineering, The Hong Kong Polytechnic University, Kowloon (Hong Kong)

    2014-03-15T23:59:59.000Z

    A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.

  3. VisIO: enabling interactive visualization of ultra-scale, time-series data via high-bandwidth distributed I/O systems

    SciTech Connect (OSTI)

    Mitchell, Christopher J [Los Alamos National Laboratory; Ahrens, James P [Los Alamos National Laboratory; Wang, Jun [UCF

    2010-10-15T23:59:59.000Z

    Petascale simulations compute at resolutions ranging into billions of cells and write terabytes of data for visualization and analysis. Interactive visuaUzation of this time series is a desired step before starting a new run. The I/O subsystem and associated network often are a significant impediment to interactive visualization of time-varying data; as they are not configured or provisioned to provide necessary I/O read rates. In this paper, we propose a new I/O library for visualization applications: VisIO. Visualization applications commonly use N-to-N reads within their parallel enabled readers which provides an incentive for a shared-nothing approach to I/O, similar to other data-intensive approaches such as Hadoop. However, unlike other data-intensive applications, visualization requires: (1) interactive performance for large data volumes, (2) compatibility with MPI and POSIX file system semantics for compatibility with existing infrastructure, and (3) use of existing file formats and their stipulated data partitioning rules. VisIO, provides a mechanism for using a non-POSIX distributed file system to provide linear scaling of 110 bandwidth. In addition, we introduce a novel scheduling algorithm that helps to co-locate visualization processes on nodes with the requested data. Testing using VisIO integrated into Para View was conducted using the Hadoop Distributed File System (HDFS) on TACC's Longhorn cluster. A representative dataset, VPIC, across 128 nodes showed a 64.4% read performance improvement compared to the provided Lustre installation. Also tested, was a dataset representing a global ocean salinity simulation that showed a 51.4% improvement in read performance over Lustre when using our VisIO system. VisIO, provides powerful high-performance I/O services to visualization applications, allowing for interactive performance with ultra-scale, time-series data.

  4. Time series of high resolution photospheric spectra in a quiet region of the Sun. II. Analysis of the variation of physical quantities of granular structures

    E-Print Network [OSTI]

    Puschmann, K G; Vazquez, M; Bonet, J A; Hanslmeier, A; 10.1051/0004-6361:20047193

    2012-01-01T23:59:59.000Z

    From the inversion of a time series of high resolution slit spectrograms obtained from the quiet sun, the spatial and temporal distribution of the thermodynamical quantities and the vertical flow velocity is derived as a function of logarithmic optical depth and geometrical height. Spatial coherence and phase shift analyzes between temperature and vertical velocity depict the height variation of these physical quantities for structures of different size. An average granular cell model is presented, showing the granule-intergranular lane stratification of temperature, vertical velocity, gas pressure and density as a function of logarithmic optical depth and geometrical height. Studies of a specific small and a specific large granular cell complement these results. A strong decay of the temperature fluctuations with increasing height together with a less efficient penetration of smaller cells is revealed. The T -T coherence at all granular scales is broken already at log tau =-1 or z~170 km. At the layers beyon...

  5. TIME-SERIES PHOTOMETRY OF GLOBULAR CLUSTERS: M62 (NGC 6266), THE MOST RR LYRAE-RICH GLOBULAR CLUSTER IN THE GALAXY?

    SciTech Connect (OSTI)

    Contreras, R. [INAF-Osservatorio Astronomico di Bologna, via Ranzani 1, 40127, Bologna (Italy); Catelan, M. [Departamento de AstronomIa y Astrofisica, Pontificia Universidad Catolica de Chile, Av. Vicuna Mackenna 4860, 782-0436 Macul, Santiago (Chile); Smith, H. A.; Kuehn, C. A. [Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824 (United States); Pritzl, B. J. [Department of Physics and Astronomy, University of Wisconsin, Oshkosh, WI 54901 (United States); Borissova, J. [Departamento de Fisica y AstronomIa, Facultad de Ciencias, Universidad de ValparaIso, Ave. Gran Bretana 1111, Playa Ancha, Casilla 5030, ValparaIso (Chile)

    2010-12-15T23:59:59.000Z

    We present new time-series CCD photometry, in the B and V bands, for the moderately metal-rich ([Fe/H] {approx_equal} -1.3) Galactic globular cluster M62 (NGC 6266). The present data set is the largest obtained so far for this cluster and consists of 168 images per filter, obtained with the Warsaw 1.3 m telescope at the Las Campanas Observatory and the 1.3 m telescope of the Cerro Tololo Inter-American Observatory, in two separate runs over the time span of 3 months. The procedure adopted to detect the variable stars was the optimal image subtraction method (ISIS v2.2), as implemented by Alard. The photometry was performed using both ISIS and Stetson's DAOPHOT/ALLFRAME package. We have identified 245 variable stars in the cluster fields that have been analyzed so far, of which 179 are new discoveries. Of these variables, 133 are fundamental mode RR Lyrae stars (RRab), 76 are first overtone (RRc) pulsators, 4 are type II Cepheids, 25 are long-period variables (LPVs), 1 is an eclipsing binary, and 6 are not yet well classified. Such a large number of RR Lyrae stars places M62 among the top two most RR Lyrae-rich (in the sense of total number of RR Lyrae stars present) globular clusters known in the Galaxy, second only to M3 (NGC 5272) with a total of 230 known RR Lyrae stars. Since this study covers most but not all of the cluster area, it is not unlikely that M62 is in fact the most RR Lyrae-rich globular cluster in the Galaxy. In like vein, thanks to the time coverage of our data sets, we were also able to detect the largest sample of LPVs known so far in a Galactic globular cluster. We analyze a variety of Oosterhoff type indicators for the cluster, including mean periods, period distribution, Bailey diagrams, and Fourier decomposition parameters (as well as the physical parameters derived therefrom). All of these indicators clearly show that M62 is an Oosterhoff type I system. This is in good agreement with the moderately high metallicity of the cluster, in spite of its predominantly blue horizontal branch morphology-which is more typical of Oosterhoff type II systems. We thus conclude that metallicity plays a key role in defining Oosterhoff type. Finally, based on an application of the 'A-method', we conclude that the cluster RR Lyrae stars have a similar He abundance as M3, although more work on the temperatures of the M62 RR Lyrae is needed before this result can be conclusively established.

  6. Engineering Instutute Seminar Series

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    Engineering Institute Engineering Instutute Seminar Series Engineering Instutute Seminar Series Calendar Contact Professional Staff Assistant Jutta Kayser Engineering Institute...

  7. ARM - Datastreams - 02wsipartradjpg

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archiven Documentation

  8. ARM - Datastreams - 02wsipartradmpg

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archiven Documentationwsipartradmpg

  9. ARM - Datastreams - 05okm

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archiven Documentationwsipartradmpg5okm

  10. ARM - Datastreams - 06fslwpdnmet

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archiven

  11. ARM - Datastreams - 06fslwpdnrass

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archivenfslwpdnrass Documentation XDC

  12. ARM - Datastreams - 06wpdnmmts

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archivenfslwpdnrass Documentation

  13. ARM - Datastreams - 10wsipartradjpg

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archivenfslwpdnrassnnns

  14. ARM - Datastreams - 10wsipartradmpg

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report Archivenfslwpdnrassnnnswsipartradmpg

  15. ARM - Datastreams - 1290bsrwpprecipavg

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Report

  16. ARM - Datastreams - 1290bsrwpwindavg

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavg Documentation Data Quality

  17. ARM - Datastreams - 1290rwpprecipcon

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavg Documentation Data

  18. ARM - Datastreams - 1290rwpprecipmom

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavg Documentation

  19. ARM - Datastreams - 1290rwpprecipspec

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavg Documentationrwpprecipspec

  20. ARM - Datastreams - 1290rwpwindcon

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavg

  1. ARM - Datastreams - 1290rwpwindmom

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavgrwpwindmom Documentation

  2. ARM - Datastreams - 1290rwpwindspec

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDaily Reportbsrwpwindavgrwpwindmom

  3. ARM - Datastreams - 1440smos

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  4. ARM - Datastreams - 15okm

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  5. ARM - Datastreams - 1smos

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  6. ARM - Datastreams - 1sprprad

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDailysmos Documentation Data Quality Plots

  7. ARM - Datastreams - 30ebbr

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  8. ARM - Datastreams - 30ecor

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? We would love to heargovInstrumentstdmaDailysmos Documentation Datammet

  9. ARM - Datastreams - 30okm

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  10. ARM - Datastreams - 30smos

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  11. ARM - Datastreams - 50rwptemp

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

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  20. ARM - Datastreams - aosmet

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  20. ARM - Datastreams - avhrrdar

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  1. ARM - Datastreams - blc

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  3. ARM - Datastreams - brs

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  5. ARM - Datastreams - ceil

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  7. ARM - Datastreams - cmh

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  1. ARM - Datastreams - dlcal1

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  7. ARM - Datastreams - dlusr

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  10. ARM - Datastreams - ecmwfsfce

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  19. ARM - Datastreams - maps60

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? WeDatastreamskasacrspeccmaskxpolDatastreamskazrspeccmaskmdcopol Documentation

  20. ARM - Datastreams - marinemet

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? WeDatastreamskasacrspeccmaskxpolDatastreamskazrspeccmaskmdcopol

  1. ARM - Datastreams - met

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? WeDatastreamskasacrspeccmaskxpolDatastreamskazrspeccmaskmdcopolDatastreamsmet Documentation

  2. ARM - Datastreams - mettiptwr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments? WeDatastreamskasacrspeccmaskxpolDatastreamskazrspeccmaskmdcopolDatastreamsmet

  3. ARM - Datastreams - mettwr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?

  4. ARM - Datastreams - mfrsr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality Plots Citation DOI:

  5. ARM - Datastreams - mmcrcal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality Plots Citation DOI:Datastreamsmmcrcal

  6. ARM - Datastreams - mmcrmom

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality Plots Citation

  7. ARM - Datastreams - mmcrmoments

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality Plots CitationDatastreamsmmcrmoments

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    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality Plots

  9. ARM - Datastreams - mmcrspeccmaskci

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality PlotsDatastreamsmmcrspeccmaskci Documentation

  10. ARM - Datastreams - mmcrspeccmaskge

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality PlotsDatastreamsmmcrspeccmaskci

  11. ARM - Datastreams - mmcrspectra

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data Quality

  12. ARM - Datastreams - moltsedassfcclass0

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data QualityDatastreamsmoltsedassfcclass0 Documentation XDC

  13. ARM - Datastreams - moltsedassfcclass1

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data QualityDatastreamsmoltsedassfcclass0 Documentation

  14. ARM - Datastreams - moltsedassndclass0

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data QualityDatastreamsmoltsedassfcclass0

  15. ARM - Datastreams - moltsedassndclass1

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation Data

  16. ARM - Datastreams - mpl

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl Documentation Data Quality Plots Citation

  17. ARM - Datastreams - mplpol

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl Documentation Data Quality Plots

  18. ARM - Datastreams - mplpolfs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl Documentation Data Quality

  19. ARM - Datastreams - mplps

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl Documentation Data

  20. ARM - Datastreams - mtsat

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl Documentation DataDatastreamsmtsat

  1. ARM - Datastreams - mwacr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl Documentation

  2. ARM - Datastreams - mwacrspeccmaskcopol

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmpl

  3. ARM - Datastreams - mwrhf

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmplDatastreamsmwr3c Documentation

  4. ARM - Datastreams - mwrlos

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmplDatastreamsmwr3c

  5. ARM - Datastreams - mwrp

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m Documentation DataDatastreamsmplDatastreamsmwr3cDatastreamsmwrp

  6. ARM - Datastreams - nav

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation Data Quality Plots Citation DOI:

  7. ARM - Datastreams - navgps

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation Data Quality Plots Citation

  8. ARM - Datastreams - ncepgfsatkflx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation Data Quality Plots

  9. ARM - Datastreams - ncepgfsatkpprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation Data Quality

  10. ARM - Datastreams - ncepgfsatksfc

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation Data

  11. ARM - Datastreams - ncepgfsatksprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation DataDatastreamsncepgfsatksprof

  12. ARM - Datastreams - ncepgfsatkzprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav Documentation

  13. ARM - Datastreams - ncepgfsbrwflx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav DocumentationDatastreamsncepgfsbrwflx

  14. ARM - Datastreams - ncepgfsbrwpprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnav

  15. ARM - Datastreams - ncepgfsbrwsfc

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnavDatastreamsncepgfsbrwsfc Documentation XDC

  16. ARM - Datastreams - ncepgfsbrwsprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnavDatastreamsncepgfsbrwsfc Documentation

  17. ARM - Datastreams - ncepgfsbrwzprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m DocumentationDatastreamsnavDatastreamsncepgfsbrwsfc

  18. ARM - Datastreams - ncepgfsdarflx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10m

  19. ARM - Datastreams - ncepgfsdarpprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof Documentation XDC documentation Data Quality

  20. ARM - Datastreams - ncepgfsdarsfc

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof Documentation XDC documentation Data

  1. ARM - Datastreams - ncepgfsdarsprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof Documentation XDC documentation

  2. ARM - Datastreams - ncepgfsdarzprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof Documentation XDC

  3. ARM - Datastreams - ncepgfsflx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof Documentation XDCDatastreamsncepgfsflx

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof Documentation

  5. ARM - Datastreams - ncepgfsmanpprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof DocumentationDatastreamsncepgfsmanpprof

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    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprof

  7. ARM - Datastreams - ncepgfsmansprof

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprofDatastreamsncepgfsmansprof Documentation XDC

  8. ARM - Datastreams - ncepgfsmanzprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprofDatastreamsncepgfsmansprof Documentation

  9. ARM - Datastreams - ncepgfsnauflx

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air Comments?Datastreamsmfr10mDatastreamsncepgfsdarpprofDatastreamsncepgfsmansprof

  10. ARM - Datastreams - ncepgfsnaupprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492air

  11. ARM - Datastreams - ncepgfsnausfc

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality Plots ARM Data Discovery Browse

  12. ARM - Datastreams - ncepgfsnausprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality Plots ARM Data Discovery

  13. ARM - Datastreams - ncepgfsnauzprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality Plots ARM Data

  14. ARM - Datastreams - ncepgfspprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality Plots ARM

  15. ARM - Datastreams - ncepgfssfc

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality Plots ARMDatastreamsncepgfssfc

  16. ARM - Datastreams - ncepgfssprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality Plots

  17. ARM - Datastreams - ncepgfszprof

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality PlotsDatastreamsncepgfszprof

  18. ARM - Datastreams - nfov

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data Quality

  19. ARM - Datastreams - ngm250

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data QualityDatastreamsnfov2ch

  20. ARM - Datastreams - nimfr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation Data

  1. ARM - Datastreams - noaaaos

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation DataDatastreamsnoaaaos Documentation Data

  2. ARM - Datastreams - noaaaosavg

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation DataDatastreamsnoaaaos Documentation

  3. ARM - Datastreams - noaaaosccn100

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation DataDatastreamsnoaaaos

  4. ARM - Datastreams - noaaradbrw

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentation DataDatastreamsnoaaaosDatastreamsnoaaradbrw

  5. ARM - Datastreams - org

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentationDatastreamsnwsupa6s Documentation

  6. ARM - Datastreams - pars2

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentationDatastreamsnwsupa6s

  7. ARM - Datastreams - pgs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC documentationDatastreamsnwsupa6sDatastreamspgs

  8. ARM - Datastreams - precnet

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDC

  9. ARM - Datastreams - prpfrsr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation Data Quality Plots Citation

  10. ARM - Datastreams - prprad

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation Data Quality Plots

  11. ARM - Datastreams - pws

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation Data Quality

  12. ARM - Datastreams - rad

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation Data QualityDatastreamsrad

  13. ARM - Datastreams - rain

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation Data

  14. ARM - Datastreams - rphcontrol

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation DataDatastreamsrphcontrol

  15. ARM - Datastreams - rphtilt

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr Documentation

  16. ARM - Datastreams - rss

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr DocumentationDatastreamsrss Documentation

  17. ARM - Datastreams - ruc60

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsr

  18. ARM - Datastreams - sashemfr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsrDatastreamssashemfr Documentation Data

  19. ARM - Datastreams - sashenir

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsrDatastreamssashemfr Documentation

  20. ARM - Datastreams - sashenirhisun

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation XDCDatastreamsprpfrsrDatastreamssashemfr

  1. ARM - Datastreams - sashenirlowsun

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc Documentation

  2. ARM - Datastreams - sashevishisun

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun Documentation Data Quality Plots

  3. ARM - Datastreams - sashevislowsun

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun Documentation Data Quality

  4. ARM - Datastreams - saszefilterbands

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun Documentation Data

  5. ARM - Datastreams - saszenir

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun Documentation DataDatastreamssaszenir

  6. ARM - Datastreams - saszevis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun Documentation

  7. ARM - Datastreams - sebs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun DocumentationDatastreamssebs

  8. ARM - Datastreams - sirs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisun

  9. ARM - Datastreams - snodep

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc DocumentationDatastreamssashevishisunDatastreamssirs60sDatastreamssnodep

  10. ARM - Datastreams - sodar

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfc

  11. ARM - Datastreams - sonde

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC documentation Data Quality Plots Citation

  12. ARM - Datastreams - sondewnpn

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC documentation Data Quality Plots

  13. ARM - Datastreams - sondewnpr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC documentation Data Quality

  14. ARM - Datastreams - sondewrpn

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC documentation Data

  15. ARM - Datastreams - sondewrpr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC documentation DataDatastreamssondewrpr

  16. ARM - Datastreams - surflog

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC documentationDatastreamssurflog

  17. ARM - Datastreams - surthref

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDC

  18. ARM - Datastreams - swacrblrhi

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDCDatastreamsswacrblrhi Documentation Data

  19. ARM - Datastreams - swacrcal

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDCDatastreamsswacrblrhi Documentation

  20. ARM - Datastreams - swacrcwrhi

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDCDatastreamsswacrblrhi

  1. ARM - Datastreams - swacrfpt

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation XDCDatastreamsswacrblrhiDatastreamsswacrfpt

  2. ARM - Datastreams - swacrhsrhi

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde Documentation

  3. ARM - Datastreams - swacrppi

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde DocumentationDatastreamsswacrppi Documentation Data Quality

  4. ARM - Datastreams - swacrspeccmaskcopol

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde DocumentationDatastreamsswacrppi Documentation Data

  5. ARM - Datastreams - swacrspeccmaskxpol

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde DocumentationDatastreamsswacrppi Documentation

  6. ARM - Datastreams - swacrvad

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde DocumentationDatastreamsswacrppi

  7. ARM - Datastreams - swacrvpt

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde DocumentationDatastreamsswacrppiDatastreamsswacrvpt

  8. ARM - Datastreams - swats

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssonde

  9. ARM - Datastreams - swatspcp

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp Documentation Data Quality Plots Citation

  10. ARM - Datastreams - sws

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp Documentation Data Quality Plots

  11. ARM - Datastreams - tdmahyg

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp Documentation Data

  12. ARM - Datastreams - tdmasize

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp Documentation DataDatastreamstdmasize

  13. ARM - Datastreams - thwaps

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp Documentation

  14. ARM - Datastreams - tlcv

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp DocumentationDatastreamstlcv Documentation

  15. ARM - Datastreams - tps

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp DocumentationDatastreamstlcv

  16. ARM - Datastreams - tsicldmask

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcp

  17. ARM - Datastreams - tsimovie

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcpDatastreamstsimovie Documentation Data

  18. ARM - Datastreams - tsiskycover

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcpDatastreamstsimovie Documentation

  19. ARM - Datastreams - tsiskyimage

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcpDatastreamstsimovie

  20. ARM - Datastreams - twr

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492airDatastreamsncepgfsnausfcDatastreamssondeDatastreamsswatspcpDatastreamstsimovieDatastreamstwr