Generic and scalable periodicity adaptation framework for time-series anomaly detection

Nowadays, multivariate time series data is increasingly collected in many large-scale application systems, which often has periodic, repetitive patterns that can be affected by advertisements, workdays, holidays, and some user behavior activities. However, existing density and distance-based anomaly detection approaches suffer from detecting anomalies related to periodicity and seasonality. To address this problem, we propose a generic and scalable adaptation framework (GSPAD) for unsupervised anomaly detection in time series with periodic patterns. Our framework mainly consists of a time series predictor and an anomaly detector. Therefore, we present a Convolutional Attention-skip Network (CASNet) as a predictor responsible for predicting both short- and long-term patterns. These two types of patterns are modeled by the CASNet combining the Convolutional Neural Network (CNN) and the Dual Branch Attention-skip Network. Moreover, the proposed anomaly detector can deduce the anomaly according to the severity of the deviations between the actual and predicted values. Compared with other related researches on public datasets, GSPAD shows better performance with an average F-score over 0.76.