Innovation Models for Stochastic Process and Their Zeros
Summary :
A relationship between two innovation representations is discussed in a stationary process, and a numerical algorithm of a new type innovation model is introduced for the time series analysis. Coefficient matrices of both innovation models are derived from correlation functions by using the singular value decomposition method of Hankel matrix and solving a Riccati type equation. Zeros of both models are also examined, since they have an important role not only in the analysis of AR model, but also in system diagnosis.
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- IEICE TRANSACTIONS on Fundamentals Vol.E74-A No.9 pp.2540-2546
- Publication Date
- 1991/09/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Special Section PAPER (Special Issue on Information Theory and Its Applications)
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Kuniharu KISHIDA, "Innovation Models for Stochastic Process and Their Zeros" in IEICE TRANSACTIONS on Fundamentals,
vol. E74-A, no. 9, pp. 2540-2546, September 1991, doi: .
Abstract: A relationship between two innovation representations is discussed in a stationary process, and a numerical algorithm of a new type innovation model is introduced for the time series analysis. Coefficient matrices of both innovation models are derived from correlation functions by using the singular value decomposition method of Hankel matrix and solving a Riccati type equation. Zeros of both models are also examined, since they have an important role not only in the analysis of AR model, but also in system diagnosis.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e74-a_9_2540/_p
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@ARTICLE{e74-a_9_2540,
author={Kuniharu KISHIDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Innovation Models for Stochastic Process and Their Zeros},
year={1991},
volume={E74-A},
number={9},
pages={2540-2546},
abstract={A relationship between two innovation representations is discussed in a stationary process, and a numerical algorithm of a new type innovation model is introduced for the time series analysis. Coefficient matrices of both innovation models are derived from correlation functions by using the singular value decomposition method of Hankel matrix and solving a Riccati type equation. Zeros of both models are also examined, since they have an important role not only in the analysis of AR model, but also in system diagnosis.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Innovation Models for Stochastic Process and Their Zeros
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2540
EP - 2546
AU - Kuniharu KISHIDA
PY - 1991
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E74-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1991
AB - A relationship between two innovation representations is discussed in a stationary process, and a numerical algorithm of a new type innovation model is introduced for the time series analysis. Coefficient matrices of both innovation models are derived from correlation functions by using the singular value decomposition method of Hankel matrix and solving a Riccati type equation. Zeros of both models are also examined, since they have an important role not only in the analysis of AR model, but also in system diagnosis.
ER -
English
