| Project/Area Number |
13558025
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Statistical science
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
KITAGAWA Genshiro The Institute of Statistical Mathematics, Director-General, 所長 (20000218)
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| Co-Investigator(Kenkyū-buntansha) |
TAMURA Yoshiyasu The Institute of Statistical Mathematics, Center for Development of Statistical Computing, Prof., 統計計算開発センター, 教授 (60150033)
HIGUCHI Tomoyuki The Institute of Statistical Mathematics, Prediction and Control, Prof., 予測制御研究系, 教授 (70202273)
SATO Seisho The Institute of Statistical Mathematics, Prediction and Control, Assoc.Prof., 予測制御研究系, 助教授 (60280525)
KAWASAKI Yoshinori The Institute of Statistical Mathematics, Prediction and Control, Assist.Prof., 予測制御研究系, 助手 (70249910)
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| Project Period (FY) |
2001 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥11,000,000 (Direct Cost: ¥11,000,000)
Fiscal Year 2004: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2003: ¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 2002: ¥4,100,000 (Direct Cost: ¥4,100,000)
Fiscal Year 2001: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Keywords | State -space model / Monte Carlo filter / self organizing model / parallel computation / nonstationary time series / time series analysis software / Bayesian model / non-Gaussian filter / 時系列解析ソフトウェア / 超多変量時系列 / 多変量ARモデル / 知識発見 / 逐次計算アルゴリズム / 一般化情報量規準 / パワー寄与率 / 予測 |
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| Research Abstract |
By using the state-space model, most of the familiar time series models can be treated. It also provides efficient recursive filtering algorithm for state estimation, prediction, smoothing and parameter estimation based on the likelihood. In this project research, we developed a time series analysis software based on this state-space approach. Further, we developed a Web-version of the softwares and applied the method to various real-world problems. The major outcomes are as follows:
1. Development of filtering and parameter estimation method.
We refined the algorithms of the Monte Carlo filter and the Gaussian-sum filter for efficient computation of conditional state distributions. We also developed parallel algorithm for computing estimation of the state and some algorithms for simultaneous estimation of the state and the unknown parameters.
2. We developed generic softwares for Monte Carlo filtering and self-organizing filter. Specific application softwares were also developed for trend estimation, estimation of stochastic volatility, trend and volatility model and power contribution analysis. Web-software for the application software on user's Web site without installing these softwares.
3. The developed methods and softwares were applied to various real-world problems such as the seismology, earth-magnetography, finance, macro-economics, marketing, human behavior, ship engineering, and various other complex systems and obtained useful results in prediction, control and knowledge discovery.
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