人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
論文
部分空間法に基づく変化点検知アルゴリズム
河原 吉伸矢入 健久町田 和雄
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ジャーナル フリー

2008 年 23 巻 2 号 p. 76-85

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In this paper, we propose a class of algorithms for detecting the change-points in time-series data based on subspace identification, which is originaly a geometric approach for estimating linear state-space models generating time-series data. Our algorithms are derived from the principle that the subspace spanned by the columns of an observability matrix and the one spanned by the subsequences of time-series data are approximately equivalent. In this paper, we derive a batch-type algorithm applicable to ordinary time-series data, i.e., consisting of only output series, and then introduce the online version of the algorithm and the extension to be available with input-output time-series data. We illustrate the superior performance of our algorithms with comparative experiments using artificial and real datasets.

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© 2008 JSAI (The Japanese Society for Artificial Intelligence)
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