TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
This addresses the problem of detecting anomalies in time series data for applications like monitoring and security, with an incremental improvement over existing methods.
The paper tackles time series anomaly detection by proposing TFAD, a model that leverages both time and frequency domains, along with decomposition and data augmentation, achieving state-of-the-art performance on benchmark datasets.
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.