Abstract: Time series anomaly detection plays a crucial role in various fields such as finance,healthcare,and industrial production.Relevant applications include detecting abnormal response codes in core banking systems within the financial sector,monitoring electroencephalographic(EEG) signals in healthcare,and providing early warnings for industrial sensor signals.In the field of multivariate time series anomaly detection,existing methods have limitations in handling complex patterns and data with sparse labels.This paper proposes a self-supervised time series anomaly detection algorithm based on time-frequency contrastive learning,which innovatively combines time-domain and frequency-domain features to build a cross-modal representation learning framework.Firstly,high-quality time-frequency consistent representations are obtained from randomly augmented samples through time-domain and frequency-domain feature extraction.Secondly,the model is optimized using a time-frequency ranking contrastive loss function and a hierarchical contrastive loss function and,which accurately control the distance between different samples,capture multi-scale information in time series,and effectively address the issue of false negative samples in contrastive learning.Finally,anomalies are identified based on sample masking and sliding window detection.Experimental results on several datasets from different fields show that the algorithm achieves state-of-the-art performance.Ablation experiments and visualization results verify the effectiveness of the proposed algorithm and its modules,providing an efficient,accurate,and robust solution for time series anomaly detection in finance,industry,and other fields.

Key words: ime series, Anomaly detection, Time-frequency contrastive learning, Multi-domain feature extraction