Discordに基づく時系列データ異常検知におけるウインドウサイズ依存性について

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/10/01

タイトル(英語): Window Size Dependency on Anomaly Detection Based on Discord for Time Series

著者名: 今村　誠（三菱電機（株）情報技術総合研究所），中村　隆顕（三菱電機（株）情報技術総合研究所）

著者名(英語): Makoto Imamura (Information Technology R&D Center, Mitsubishi Electric Corporation), Takaaki Nakamura (Information Technology R&D Center, Mitsubishi Electric Corporation)

キーワード: 異常検知，ディスコード，異常分類，設備保全　　Anomaly Detection，Discord，Anomaly Classification，Facility Maintenance

要約(英語): A large facility such as plants and buildings have many and various sensors so that “training data less” and “parameter tuning free” are required for anomaly detection. Discord method that finds the nearest neighbor subsequence with high speed is a state of the art satisfying the above needs. However, the selection of window size for discord is a remaining issue. We notice that discord behavior in transient state is different from that in steady state and show that the optimal window size depends on the anomaly shape because of a discord-shift in transient state (In a transient state, the nearest neighbor subsequence of an anomaly subsequence shifts from the normal subsequence corresponding to the anomaly subsequence originally.). We also define an evaluation index to compare the detection accuracy for anomalies with different window sizes and specify the condition when a discord-shift occurs in a transient state. Lastly, we consider a window size tuning method on the basis of the above knowledge.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.10 （2015） 特集：産業界を支える情報通信技術

本誌掲載ページ: 1244-1254 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/10/135_1244/_article/-char/ja/