2003 Fiscal Year Final Research Report Summary
Development of Knowledge Discovery Systems by using the Hierarchical Bayesian Time Series Models
| Project/Area Number |
12558023
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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 |
HIGUCHI Tomoyuki The Institute of Statistical Mathematics, Department of Prediction and Control, Prof., 予測制御研究系, 教授 (70202273)
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| Co-Investigator(Kenkyū-buntansha) |
SATO Seisho The Institute of Statistical Mathematics, Department of Prediction and Control, Assoc.Prof., 予測制御研究系, 助教授 (60280525)
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)
KITAGAWA Genshiro The Institute of Statistical Mathematics, Department of Prediction and Control, Director-General, 所長 (20000218)
SHIMODAIRA Hidetoshi Tokyo Institute of Technology, Information Science and Engineering, Lecturer, 情報理工学研究科, 講師 (00290867)
KAWASAKI Yoshinori The Institute of Statistical Mathematics, Department of Prediction and Control, Assist.Prof., 予測制御研究系, 助手 (70249910)
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| Project Period (FY) |
2000 – 2003
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| Keywords | Hierarchical Bayesian model / Self-organizing / Generalized state space model / Particle filter / Hyperparameter / Count data / Monte Carlo method / Model averaging |
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| Research Abstract |
In this project, we dealt with a removal of the artificial noises that is a stumbling block in the effort to perform an automatic procedure for knowledge discovery from a large-scale time series data. More specifically, we focused on the problems to exclude a rapid change in a trend (background mean) due to changes in sensitivity of the observation instruments, and to identify an outlier. The self-organizing state space model which belongs to the hierarchical Bayesian model has been employed to solve these problems. It is capable of estimating the trend component even if the noise component in a time series shows a time-dependent structure ; ex., its variance depends on time. The program that we developed allows us to detect the tune-dependent mean structure automatically for a large-scale time series datasets. We hope this program will open a door for us to re-analyze huge accumulated dataset that has not been examined in detail owing to an apparent signal contamination by various noises. We have already post this program with an explanation for usage on Web.
http://tswww.ism.ac.jp/higuchi/index e/Soft/index.htm
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