Time-frequency contrastive learning with context modeling for time series anomaly prediction.

Abstract

The identification of anomalies in multivariate time series data from Internet of Things systems is essential for intelligent operation and maintenance. However, most research focuses solely on anomaly detection task after a fault has occurred, during which time equipment or economic losses may have already occurred. To provide early warnings before anomalies manifest, researchers investigate the more challenging task of time series anomaly prediction. Owing to the weak signs of anomaly precursors, existing anomaly detection models perform poorly in anomaly prediction tasks. To address this issue, a novel Time-Frequency contrastive framework with context modeling for time series Anomaly Prediction (TFAP) is proposed. TFAP introduces a time-frequency contrastive structure that uses a Transformer network to align cross-view semantic representations based on time-frequency consistency, thereby capturing discriminative features in time series data. Different views of the subtle precursor signals exhibit distinct representation discrepancies in the learned embedding space. Furthermore, a context modeling module is proposed to learn the contextual dependencies between current and future data, thereby enhancing the sensitivity of the model to precursors. Extensive experiments on five real-world datasets demonstrate that the proposed TFAP achieves an average F1 score improvement of 8.43 %, significantly outperforming other state-of-the-art methods.