MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
This addresses the need for better anomaly prediction in time series for applications like monitoring systems, though it appears incremental as it builds on existing unsupervised methods.
The paper tackles the problem of predicting and detecting anomalies in time series data, which is challenging due to diverse reaction times and lack of labels, and reports that MultiRC outperforms state-of-the-art methods on seven benchmark datasets for both tasks.
Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predicting anomalies is particularly challenging due to the diverse reaction time and the lack of labeled data. To address these challenges, we propose MultiRC to integrate reconstructive and contrastive learning for joint learning of anomaly prediction and detection, with multi-scale structure and adaptive dominant period mask to deal with the diverse reaction time. MultiRC also generates negative samples to provide essential training momentum for the anomaly prediction tasks and prevent model degradation. We evaluate seven benchmark datasets from different fields. For both anomaly prediction and detection tasks, MultiRC outperforms existing state-of-the-art methods.