融合重建与对比学习，实现多尺度异常预测与检测联合优化
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
- 设计多尺度结构与自适应主导周期掩码，应对反应时间差异
- 生成负样本提升预测能力，避免模型性能退化
- 在7个跨领域数据集上均超越现有最优方法
针对无监督时间序列异常检测中未来异常预测研究不足的问题，本文提出MultiRC，通过联合学习异常预测与检测任务，结合重建与对比学习。引入多尺度结构和自适应主导周期掩码，有效处理不同反应时间的挑战；同时生成负样本以提供训练驱动力并防止模型退化。在来自不同领域的七个基准数据集上评估，结果表明MultiRC在异常预测与检测两项任务上均显著优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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