Change-point detection in time-series data based on subspace identification

Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

81 被引用数 (Scopus)

抄録

In this paper, we propose series of algorithms for detecting change points in time-series data based on subspace identification, meaning a geometric approach for estimating linear state-space models behind 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 an 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 effectiveness of our algorithms with comparative experiments using some artificial and real datasets.

本文言語英語
ホスト出版物のタイトルProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
ページ559-564
ページ数6
DOI
出版ステータス出版済み - 2007
外部発表はい
イベント7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, 米国
継続期間: 10月 28 200710月 31 2007

出版物シリーズ

名前Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(印刷版)1550-4786

会議

会議7th IEEE International Conference on Data Mining, ICDM 2007
国/地域米国
CityOmaha, NE
Period10/28/0710/31/07

!!!All Science Journal Classification (ASJC) codes

  • 工学一般

フィンガープリント

「Change-point detection in time-series data based on subspace identification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル