文章基本信息

标题：Change-Point Detection Algorithms based on Subspace Methods
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作者：Yoshinobu KAWAHARA ; Takehisa YAIRI ; Kazuo MACHIDA 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2008
卷号：23
期号：2
页码：76-85
DOI：10.1527/tjsai.23.76
出版社：The Japanese Society for Artificial Intelligence
摘要：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.
关键词：change-point detection ; time-series data ; subspace methods ; state-space model ; online algorithm