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 DVARmodel of a continuous time,multivariate, linearMarkov 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.
| Original language | English |
|---|---|
| Article number | 24 |
| Journal | ACM Transactions on Intelligent Systems and Technology |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Dec 1 2015 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Artificial Intelligence
Cite this
A novel continuous and structural VAR modeling approach and its application to reactor noise analysis. / Demeshko, Marina; Washio, Takashi; Kawahara, Yoshinobu; Pepyolyshev, Yuriy.
In: ACM Transactions on Intelligent Systems and Technology, Vol. 7, No. 2, 24, 01.12.2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A novel continuous and structural VAR modeling approach and its application to reactor noise analysis
AU - Demeshko, Marina
AU - Washio, Takashi
AU - Kawahara, Yoshinobu
AU - Pepyolyshev, Yuriy
PY - 2015/12/1
Y1 - 2015/12/1
N2 - 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 DVARmodel of a continuous time,multivariate, linearMarkov 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.
AB - 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 DVARmodel of a continuous time,multivariate, linearMarkov 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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U2 - 10.1145/2710025
DO - 10.1145/2710025
M3 - Article
AN - SCOPUS:84952948724
VL - 7
JO - ACM Transactions on Intelligent Systems and Technology
JF - ACM Transactions on Intelligent Systems and Technology
SN - 2157-6904
IS - 2
M1 - 24
ER -