Grant-in-Aid for Scientific Research (B)
|Allocation Type||Single-year Grants|
|Research Institution||The Institute of Statistical Mathematics|
KITAGAWA Genshiro The Institute of Statistical Mathematics, Director General, 所長 (20000218)
TAMURA Yoshiyasu The Institute of Statistical Mathematics, Department of Data Science, Prof., データ科学研究系, 教授 (60150033)
HIGUCHI Tomoyuki The Institute of Statistical Mathematics, Department of Statistical Modeling, Prof., モデリング研究系, 教授 (70202273)
SATO Seisho The Institute of Statistical Mathematics, Department of Data Science, Assoc. Prof., データ科学研究系, 助教授 (60280525)
KAWASAKI Yoshinori The Institute of Statistical Mathematics, Department of Statistical Modeling, Assoc. Prof., モデリング研究系, 助教授 (70249910)
KONISHI Sadanori Graduate School of Mathematics Kyushu University, Prof., 大学院・数理学研究院, 教授 (40090550)
|Project Period (FY)
2002 – 2005
Completed(Fiscal Year 2005)
|Budget Amount *help
¥12,700,000 (Direct Cost : ¥12,700,000)
Fiscal Year 2005 : ¥2,700,000 (Direct Cost : ¥2,700,000)
Fiscal Year 2004 : ¥3,200,000 (Direct Cost : ¥3,200,000)
Fiscal Year 2003 : ¥3,200,000 (Direct Cost : ¥3,200,000)
Fiscal Year 2002 : ¥3,600,000 (Direct Cost : ¥3,600,000)
|Keywords||high-dimensional time series / multivariate AR model / state-space model / knowledge discovery / sequential computation / generalized information criterion / power contribution / prediction / 一般化情報量基準|
In the areas such as earth science, economics, finance, marketing, life science and environmental science, huge amount of data are being obtained. To develop tools for the extraction of useful information from these massive data, we performed research on the computational methods for fitting multivariate time series with very high-dimension, various sequential filtering algorithms and a method of detecting cosal relation between variables based on estimated multivariate time series model. The methods were applied various problems in real word. The major outcomes are as follows :
1.Development of methods for fitting high-dimensional AR model.
By using forward and backward prediction error sequences, a very efficient method for estimating AR coefficient matrices was developed. An algorithm for efficient computation on parallel processor is also developed.
1.Filtering algorithms for high-dimensional state-space model
To develop an efficient filtering method that can be applied very-dimensiona
l state-space models, various algorithms based on information matrices, square-root algorithm, innovation type algorithm and approximation methods were considered. For the extension to nonlinear non-Gaussian state-space models, a new method of performing Gaussian-mixture approximation is also developed.
2.Parallel Monte Carlo filter was developed for efficient sequential Monte Carlo filtering for complex problems based on parallel execution of many MCF. By numerical experiments, it was shown that the developed algorithm is very suitable for parallel computation and still maintains equivalent accuracy.
3.Applications to real-world problems
(1)A method of computing generalized power contribution from estimated AR model was modified and applied to various data sets such as electric power plant data and CDS (credit default swap) data and obtained useful information.
(2)By the modeling from high-dimensional time series obtained from ocean bottom seismograph array, analysis method for underground structure was developed. Less