Periodicity Association Based Contrastive Learning for Time Series Anomaly Detection

Jiawei Xu, Zhaowei Zhu, Weiwei Ye, Ning Gui

Abstract: Unsupervised timeseries anomaly detection (UTAD) aims to identify abnormal patterns within time series data and is of immense importance in extensive applications. Contrastive learning has been seen as an effective candidate for UTAD as it can learn invariants existing in two contrastive views. However, as most time series contain complex multi-periodic and non-periodic signals, the huge difference between sequences with different periodicity would make contrastive learning hard to learn representative temporal and/or spatial patterns that are essential for anomaly detection. To address this issue, we propose PACdetector, a periodicity association-based contrastive framework for UTAD. Specifically, we perceive time series as an aggregation of various periodic sequences, and for each point in the periodical sequences, we employ self-attention maps to calculate its association with points within the period (intraperiod association) and points at the same phase across different periods (interperiod association). We then perform contrastive learning between the two associations to preserve temporal consistency and obtain a distinguishable criterion between normal points and anomalies, which we refer to as Periodicity Association Discrepancy. Extensive experiments show that PACdetector outperforms various state-of-the-art algorithms, achieving the best performance across six benchmark datasets.