关键词: 时间序列, 小波变换, 二次回归模型, 多尺度, 异常模式

Abstract: The limitations of the linear pattern segmentation are mostly used for anomaly pattern detection of time series, a multi-scale anomaly detection method in time series based on multi-scale feature of Haar wavelet transform and pattern segmentation technique of time series is proposed. In this method, time series is compressed and decomposed on different scales by wavelet transform, and then time series of decomposed is segmented into variable length patterns by quadratic regression model. The anomaly value of all patterns are calculated. Finally, the original time series is reconstructed and the anomaly patterns in the original time series are detected. The experimental results show that this method can detect effectively the anomaly and multi-scale abnormal patterns can be detected from the compressed time series with different decomposition stages.

Key words: time series, wavelet transform, quadratic regression model, multi-scale, outlier pattern