Multi-Scale Anomaly Detection for Time Series with Attention-based Recurrent Autoencoders

Qingning Lu, Wenzhong Li, Chuanze Zhu, Yizhou Chen, Yinke Wang, Zhijie Zhang, Linshan Shen, Sanglu Lu

Abstract: Anomaly detection on time series is an important research topic in data mining, which has a wide range of applications in financial markets, biological data, information technology, manufacturing system, etc. However, the existing time series anomaly detection methods mainly capture temporal features from a single-scale viewpoint, which cannot detect multi-scale anomalies effectively. In this paper, we propose a novel approach of Multi-scale Anomaly Detection for Time Series (MAD-TS) with an attention-based recurrent autoencoder model to solve the above problem. The proposed method adopts a hierarchically connected recurrent encoder to extract the features of a time series from different levels. The multi-scale features are then fused by a hierarchical decoder with attention mechanism to reconstruct the original sequence at different scales. Based on the reconstruction errors at multiple scales, anomaly scores can be learned for different data points, which can be used to infer the anomaly status of the time series. Extensive experiments based on five open time series datasets show that the proposed MAD-TS method achieves significant performance improvement on anomaly detection compared to the state-of-the-arts.