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Method for Anomaly Detection in Time Series Data Based on Spectral Partitioning
Method for Anomaly Detection in Time Series Data Based on Spectral Partitioning
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机译:基于谱划分的时间序列数据异常检测方法
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摘要
Anomalies in real time series are detected by first determining a similarity matrix of pairwise similarities between pairs of normal time series data. A spectral clustering procedure is applied to the similarity matrix to partition variables representing dimensions of the time series data into mutually exclusive groups. A model of normal behavior is estimated for each group. Then, for the real time series data, an anomaly score is determined, using the model for each group, and the anomaly score is compared to a predetermined threshold to signal the anomaly.
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