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A Novel Continuous and Structural VAR Modeling Approach and Its Application to Reactor Noise Analysis

Authors:

Marina Demeshko,

Takashi Washio,

Yoshinobu Kawahara,

Yuriy PepyolyshevAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 2

Article No.: 24, Pages 1 - 22

https://doi.org/10.1145/2710025

Published: 26 November 2015 Publication History

Abstract

A vector autoregressive model in discrete time domain (DVAR) is often used to analyze continuous time, multivariate, linear Markov systems through their observed time series data sampled at discrete timesteps. Based on previous studies, the DVAR model is supposed to be a noncanonical representation of the system, that is, it does not correspond to a unique system bijectively. However, in this article, we characterize the relations of the DVAR model with its corresponding Structural Vector AR (SVAR) and Continuous Time Vector AR (CTVAR) models through a finite difference method across continuous and discrete time domain. We further clarify that the DVAR model of a continuous time, multivariate, linear Markov system is canonical under a highly generic condition. Our analysis shows that we can uniquely reproduce its SVAR and CTVAR models from the DVAR model. Based on these results, we propose a novel Continuous and Structural Vector Autoregressive (CSVAR) modeling approach to derive the SVAR and the CTVAR models from their DVAR model empirically derived from the observed time series of continuous time linear Markov systems. We demonstrate its superior performance through some numerical experiments on both artificial and real-world data.

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Cited By

Park JChow SEpskamp SMolenaar P(2024)Subgrouping with Chain Graphical VAR ModelsMultivariate Behavioral Research10.1080/00273171.2023.228905859:3(543-565)Online publication date: 13-Feb-2024
https://doi.org/10.1080/00273171.2023.2289058
Park JFisher ZChow SMolenaar P(2023)Evaluating Discrete Time Methods for Subgrouping Continuous ProcessesMultivariate Behavioral Research10.1080/00273171.2023.2235685(1-13)Online publication date: 17-Aug-2023
https://doi.org/10.1080/00273171.2023.2235685
Dubi CAtar R(2018)Modeling neutron count distribution in a subcritical core by stochastic differential equationsAnnals of Nuclear Energy10.1016/j.anucene.2017.09.040111(608-615)Online publication date: Jan-2018
https://doi.org/10.1016/j.anucene.2017.09.040

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 2

Special Issue on Causal Discovery and Inference

January 2016

270 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2850424

Editor:
Yu Zheng
Microsoft Research, China

Issue’s Table of Contents

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2015

Accepted: 01 December 2014

Revised: 01 October 2014

Received: 01 July 2014

Published in TIST Volume 7, Issue 2

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Cited By

Park JChow SEpskamp SMolenaar P(2024)Subgrouping with Chain Graphical VAR ModelsMultivariate Behavioral Research10.1080/00273171.2023.228905859:3(543-565)Online publication date: 13-Feb-2024
https://doi.org/10.1080/00273171.2023.2289058
Park JFisher ZChow SMolenaar P(2023)Evaluating Discrete Time Methods for Subgrouping Continuous ProcessesMultivariate Behavioral Research10.1080/00273171.2023.2235685(1-13)Online publication date: 17-Aug-2023
https://doi.org/10.1080/00273171.2023.2235685
Dubi CAtar R(2018)Modeling neutron count distribution in a subcritical core by stochastic differential equationsAnnals of Nuclear Energy10.1016/j.anucene.2017.09.040111(608-615)Online publication date: Jan-2018
https://doi.org/10.1016/j.anucene.2017.09.040

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