让模型预测未来异常发生时间，提升时序数据预警能力
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
- 用异常感知的预测机制学习异常间关联关系
- 通过可学习的异常提示池模拟多样异常模式，提升检测鲁棒性
- 适合需要提前预警异常的工业监控与金融风控场景
近期，预测未来异常事件已成为应对现实需求的重要方向。然而，精确预测异常发生的具体时间点（即异常预测，AP）仍缺乏深入研究。现有方法仅关注即时异常检测，无法对未来的异常进行精准定位。为此，本文提出新型框架Anomaly to Prompt（A2P），包含异常感知预测（AAF）和合成异常提示（SAP）。为使模型具备预测异常时间点的能力，采用学习异常间关系的策略；为增强异常检测鲁棒性，SAP引入可学习的异常提示池（APP），通过信号自适应提示模拟多样化异常模式。在多个真实世界数据集上的综合实验表明，A2P显著优于当前最优方法，具备准确预测未来异常的能力。代码已开源：https://github.com/KU-VGI/AP。
原文摘要 · Abstract (English)
Recently, forecasting future abnormal events has emerged as an important scenario to tackle real-world necessities. However, the solution of predicting specific future time points when anomalies will occur, known as Anomaly Prediction (AP), remains under-explored. Existing methods dealing with time series data fail in AP, focusing only on immediate anomalies or failing to provide precise predictions for future anomalies. To address the AP task, we propose a novel framework called Anomaly to Prompt (A2P), comprised of Anomaly-Aware Forecasting (AAF) and Synthetic Anomaly Prompting (SAP). To enable the forecasting model to forecast abnormal time points, we adopt a strategy to learn the relationships of anomalies. For the robust detection of anomalies, our proposed SAP introduces a learnable Anomaly Prompt Pool (APP) that simulates diverse anomaly patterns using signal adaptive prompt. Comprehensive experiments on multiple real-world datasets demonstrate the superiority of A2P over state-of-the-art methods, showcasing its ability to predict future anomalies. Our implementation code is available at https://github.com/KU-VGI/AP.
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