高次局所自己相関特徴による多チャンネル時系列データからの異常検知

概要

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Abnormality detection in multi-channel time-series data is important for detecting and understanding changes of states in the data source. We propose a method for automatically detecting abnormality (unusualness) by using correlation-based features of multi-channel time-series data. In general, the feature vectors of normal patterns, which occur frequently in time-series, form the low-dimensional subspace in the feature space. The proposed method measures the abnormality as deviation from the subspace. In the experiment of abnormality detection in electrocardiogram, the proposed method can appropriately detect changes that a doctor also judged to be abnormal.
2009-07-01