2012 Fiscal Year Final Research Report
Time series clustering with Dirichlet process mixtures of ARMA models
| Project/Area Number |
22500261
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Hiroshima City University |
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Principal Investigator |
SUEMATSU Nobuo 広島市立大学, 情報科学研究科, 准教授 (70264942)
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| Project Period (FY) |
2010 – 2012
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| Keywords | 時系列解析 |
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| Research Abstract |
ARMA models are parsimonious stochastic models for time series. Given a set of time series, we can cluster them by regarding that they were drawn from a mixture of ARMA models and by fitting the model to them, where if the mixture model is a Dirichlet process mixture, the number of the clusters can be simultaneously estimated. In this work, we have developed a Markov Chain Monte Carlo method to fit a Dirichlet process mixture of ARMA models to a set of time series.
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Research Products
(10 results)
