MultiRC：基于多尺度重构对比的时间序列异常预测与检测联合学习
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
AI总结：
本文提出MultiRC方法，通过多尺度重构对比学习联合实现时间序列异常预测与检测，利用自适应主导周期掩码和负样本生成应对反应时间多样及标注缺失问题，在七个基准数据集上超越现有方法。
摘要：
针对无监督时间序列异常检测，已有许多方法被提出。尽管取得了一定进展，但关于预测未来异常的研究仍然相对匮乏。由于反应时间多样且缺乏标注数据，异常预测尤其具有挑战性。为应对这些挑战，我们提出MultiRC，将重构学习与对比学习相结合，用于异常预测与检测的联合学习，并利用多尺度结构和自适应主导周期掩码来处理多样化的反应时间。MultiRC还生成负样本，为异常预测任务提供必要的训练动量并防止模型退化。我们在来自不同领域的七个基准数据集上进行了评估。在异常预测和检测任务中，MultiRC均优于现有的最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
