Anomaly Detection in Quasi-Periodic Time Series Based on Automatic Data Segmentation and Attentional LSTM-CNN

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to achieve a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3 percent accuracy, TCQSA outperforms two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. Additionally, the effectiveness of the attention mechanisms is quantitatively and qualitatively demonstrated.