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Title: Automatic peak interval, relative volatility, and relative amplitude detection in high-volume temporal data

Abstract

Various technologies pertaining to automatic relative volatility and relative amplitude detection are described herein. A spectral density of a geospatial temporal dataset is computed, and one or more frequencies of the dataset are identified. A volatility period of interest is calculated based upon the frequencies, and volatility thresholds are computed based upon the volatility period of interest. One or more periods of potential interest are detected in the dataset based upon the geospatial temporal data and the volatility thresholds. An indication of the periods of interest, an occurrence of an event captured in the dataset, or a prediction of an occurrence of an event that is of potential interest to an analyst is output.

Inventors:
;
Issue Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1568403
Patent Number(s):
10303841
Application Number:
15/077,584
Assignee:
National Technology & Engineering Solutions of Sandia, LLC (Albuquerque, NM)
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Patent
Resource Relation:
Patent File Date: 03/22/2016
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Ochoa, Edward M., and Czuchlewski, Kristina Rodriguez. Automatic peak interval, relative volatility, and relative amplitude detection in high-volume temporal data. United States: N. p., 2019. Web.
Ochoa, Edward M., & Czuchlewski, Kristina Rodriguez. Automatic peak interval, relative volatility, and relative amplitude detection in high-volume temporal data. United States.
Ochoa, Edward M., and Czuchlewski, Kristina Rodriguez. Tue . "Automatic peak interval, relative volatility, and relative amplitude detection in high-volume temporal data". United States. https://www.osti.gov/servlets/purl/1568403.
@article{osti_1568403,
title = {Automatic peak interval, relative volatility, and relative amplitude detection in high-volume temporal data},
author = {Ochoa, Edward M. and Czuchlewski, Kristina Rodriguez},
abstractNote = {Various technologies pertaining to automatic relative volatility and relative amplitude detection are described herein. A spectral density of a geospatial temporal dataset is computed, and one or more frequencies of the dataset are identified. A volatility period of interest is calculated based upon the frequencies, and volatility thresholds are computed based upon the volatility period of interest. One or more periods of potential interest are detected in the dataset based upon the geospatial temporal data and the volatility thresholds. An indication of the periods of interest, an occurrence of an event captured in the dataset, or a prediction of an occurrence of an event that is of potential interest to an analyst is output.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {5}
}

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