融合小波分解的多尺度时间序列异常检测

Multiscale time series anomaly detection incorporating wavelet decomposition

时间序列异常检测是时间序列分析中的重要任务之一,然而现实世界中多维时间序列的异常检测任务存在时间模式复杂、表征学习困难等问题。针对上述问题,提出一种融合小波分解的多尺度时间序列异常检测(WMAD)方法。具体地,以多时间窗口的方式,将时间序列的时间模式统一融合入二维堆叠的时间窗口中,增强多时间模式提取能力;同时,从频域角度引入小波变换,将原始序列分解为蕴含不同频率分量的时间变化模式,从长时间的趋势变化和短时间的瞬时变化角度捕捉复杂时间模式;借鉴卷积网络的特征提取能力,采用多尺度卷积网络自适应地聚合不同尺度的时序特征;增加包含空间和通道两种注意力机制的注意力模块,在增强多尺度特征提取能力的基础上提高关键信息的提取能力,进而提高精度。在SWaT(Secure Water Treatment)、SMD(Server Machine Dataset)和MSL(Mars Science Laboratory)等5个公共数据集上的异常检测结果显示,WMAD方法的F1值与MSCRED(MultiScale Convolutional Recurrent Encoder-Decoder)方法相比提高了3.62~9.44个百分点;与TranAD(deep Transformer networks for Anomaly Detection)方法相比提高了3.86~11.00个百分点,与其他代表性方法相比也有所提高。实验结果表明,WMAD方法能够捕获时间序列中的复杂时间模式,缓解表征困难问题,同时具有较好的异常检测性能。

Time series anomaly detection is one of the important tasks in time series analysis,but there are problems such as complex time patterns and difficult representation learning in real world multi-dimensional tasks.A WMAD(Wavelet transform for Multiscale time series Anomaly Detection)method incorporating wavelet decomposition was proposed.Specifically,the multi-temporal pattern extraction capability was enhanced through fusing the temporal patterns of time series uniformly into 2D stacked time windows in a multi-temporal window approach.At the same time,wavelet transform was introduced from the frequency domain perspective to decompose the original sequence into time-varying patterns with different frequency components to capture complex time patterns from the viewpoints of long-term trend changes and short-term transient changes.Based on the feature extraction capability of convolutional networks,a multiscale convolutional network was used to adaptively aggregate time series features at different scales.By adding the attention module containing both spatial and channel attention mechanisms,the extraction of key information was improved based on the enhancement of multiscale feature extraction capability,and thus the accuracy was improved.The anomaly detection results on five public datasets such as SWaT(Secure Water Treatment),SMD(Server Machine Dataset)and MSL(Mars Science Laboratory)show that the F1 values of WMAD method is 3.62 to 9.44 percentage points higher than those of the MSCRED(MultiScale Convolutional Recurrent Encoder-Decoder)method,3.86 to 11.00 percentage points higher than those of the TranAD(deep Transformer networks for Anomaly Detection)method,and higher than those of other representative methods.The experimental results show that the WMAD method can capture complex temporal patterns in time series and alleviate the problem of difficult representation while having good anomaly detection performance.