Change-point detection algorithms based on subspace methods

Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)76-84
Number of pages9
JournalTransactions of the Japanese Society for Artificial Intelligence
Issue number2
Publication statusPublished - 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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