Deciding on the best (in this case) approach to time-series forecasting

Pack, D. J.

Title: Deciding on the best (in this case) approach to time-series forecasting

Full Record
Other Related Research

Abstract

This paper was motivated by a Decision Sciences article (v. 10, no. 2, 232-244(April 1979)) that presented comparisons of the adaptive estimation procedure (AEP), adaptive filtering, the Box-Jenkins (BJ) methodology, and multiple regression analysis as they apply to time-series forecasting with single-series models. While such comparisons are to be applauded in general, it is demonstrated that the empirical comparisons of the above paper are quite misleading with respect to choosing between the AEP and BJ approaches. This demonstration is followed by a somewhat philosophical discussion on comparison-of-methods techniques.

Authors:: Pack, D. J.

Publication Date:: Tue Jan 01 00:00:00 EST 1980

Research Org.:: Oak Ridge National Lab., TN (USA)

OSTI Identifier:: 5361471

Report Number(s):: CONF-800571-1

DOE Contract Number:: W-7405-ENG-26

Resource Type:: Conference

Resource Relation:: Conference: Canadian Operations Research Society meeting, Quebec, Canada, 20 May 1980

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; TIME-SERIES ANALYSIS; COMPARATIVE EVALUATIONS; DECISION MAKING; ERRORS; FORECASTING; REGRESSION ANALYSIS; MATHEMATICS; STATISTICS; 990200* - Mathematics & Computers

Citation Formats


                    Pack, D. J. Deciding on the best (in this case) approach to time-series forecasting.  United States: N. p., 1980. 
        Web.

Copy to clipboard


                    Pack, D. J. Deciding on the best (in this case) approach to time-series forecasting.  United States.

Copy to clipboard


                    Pack, D. J. 1980.  
        "Deciding on the best (in this case) approach to time-series forecasting".  United States.  https://www.osti.gov/servlets/purl/5361471.

Copy to clipboard


                    
@article{osti_5361471,

  title        = {Deciding on the best (in this case) approach to time-series forecasting},

  author       = {Pack, D. J.},

  abstractNote = {This paper was motivated by a Decision Sciences article (v. 10, no. 2, 232-244(April 1979)) that presented comparisons of the adaptive estimation procedure (AEP), adaptive filtering, the Box-Jenkins (BJ) methodology, and multiple regression analysis as they apply to time-series forecasting with single-series models. While such comparisons are to be applauded in general, it is demonstrated that the empirical comparisons of the above paper are quite misleading with respect to choosing between the AEP and BJ approaches. This demonstration is followed by a somewhat philosophical discussion on comparison-of-methods techniques.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/5361471},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Jan 01 00:00:00 EST 1980},

  month        = {Tue Jan 01 00:00:00 EST 1980}

}

Copy to clipboard

Conference:

View Conference (0.74 MB)

Other availability

Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

Compensated Box-Jenkins transfer function for short term load forecast

Conference Breipohl, A; Yu, Z; Lee, F - Proceedings of the American Power Conference; (United States)

In the past years, the Box-Jenkins ARIMA method and the Box-Jenkins transfer function method (BJTF) have been among the most commonly used methods for short term electrical load forecasting. But when there exists a sudden change in the temperature, both methods tend to exhibit larger errors in the forecast. This paper demonstrates that the load forecasting errors resulting from either the BJ ARIMA model or the BJTF model are not simply white noise, but rather well-patterned noise, and the patterns in the noise can be used to improve the forecasts. Thus a compensated Box-Jenkins transfer method (CBJTF) is proposed tomore »« less
Price of gasoline: forecasting comparisons. [Box-Jenkins, econometric, and regression methods]

Journal Article Bopp, A; Neri, J

Gasoline prices are simulated using three popular forecasting methodologies: A Box--Jenkins type method, an econometric method, and a regression method. One-period-ahead and 18-period-ahead comparisons are made. For the one-period-ahead method, a Box--Jenkins type time-series model simulated best, although all do well. However, for the 18-period simulation, the econometric and regression methods perform substantially better than the Box-Jenkins formulation. A rationale for and implications of these results ae discussed. 11 references.
Practical overview of ARIMA models for time-series forecasting

Technical Report Pack, D

Single series analysis methodology is illustrated. The commentary summarizes the Box-Jenkins philosophy and the ARIMA model structure, with particular emphasis on practical aspects of application, forecast interpretation, strengths weaknesses, and comparison to other time series forecasting approaches. (GHT)
Time-Series Forecast Modeling on High-Bandwidth Network Measurements

Journal Article Yoo, Wucherl; Sim, Alex - Journal of Grid Computing

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore »« less
https://doi.org/10.1007/s10723-016-9368-9

Full Text Available
Bayesian forecasting for adaptive seasonal short-term load forecasting models with a comparative study

Miscellaneous Ba-Fail, A

Short-term electric load forecasting is the prediction of hourly electric power demand for a service area of a utility. These forecasts of megawatt demand are used to perform scheduling and dispatching of generation units. Over the past twenty years, numerous statistical and control methods have been proposed to generate these forecasts. Nearly all these methods, however, are inadequate in how they handle the season to season variability exhibited by the load data as a function of weather variations. In this study a structural model is proposed that can be used for all seasons. It is an adaptation of Harrison-Stevens Bayesianmore »« less

Similar Records