Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.

本文提出了一种基于时间-频率分析的时序异常检测模型，TFAD，能够利用时间和频率域中的信息来提高性能，并在设计的时频架构中加入了时序分解和数据增强机制，以进一步提高性能和可解释性。经实证研究表明，该方法在单变量和多变量时序异常检测任务中取得了最先进的性能表现。

TFAD: 一种基于时频分解的时间序列异常检测架构