A novel structural AR modeling approach for a continuous time linear Markov system

Marina Demeshko, Takashi Washio, Yoshinobu Kawahara

研究成果: Contribution to conferencePaper査読

2 被引用数 (Scopus)

抄録

We often use a discrete time vector autoregressive (DVAR) model to analyse continuous time, multivariate, linear Markov systems through their time series data sampled at discrete time steps. However, the DVAR model has been considered not to be structural representation and hence not to have bijective correspondence with system dynamics in general. In this paper, we characterize the relationships of the DVAR model with its corresponding structural vector AR (SVAR) and continuous time vector AR (CVAR) models through finite difference approximation of time differentials. Our analysis shows that the DVAR model of a continuous time, multivariate, linear Markov system bijectively corresponds to the system dynamics. Further we clarify that the SVAR and the CVAR models are uniquely reproduced from their DVAR model under a highly generic condition. Based on these results, we propose a novel Continuous time and Structural Vector AutoRegressive (CSVAR) modeling approach for continuous time, linear Markov systems to derive the SVAR and the CVAR models from their DVAR model empirically derived from the observed time series. We demonstrate its superior performance through some numerical experiments on both artificial and real world data.

本文言語英語
ページ104-113
ページ数10
DOI
出版ステータス出版済み - 2013
外部発表はい
イベント2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, 米国
継続期間: 12 7 201312 10 2013

会議

会議2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
Country米国
CityDallas, TX
Period12/7/1312/10/13

All Science Journal Classification (ASJC) codes

  • Software

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