何时将失败？：基于异常提示预测时间序列未来异常
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
AI总结：
针对时间序列异常预测任务，提出A2P框架，通过异常关系学习与异常提示池模拟，实现对未来异常时间点的精确预测，优于现有方法。
摘要：
近期，预测未来异常事件已成为应对现实需求的重要场景。然而，预测异常将发生的具体未来时间点的解决方案（即异常预测，AP）仍研究不足。处理时间序列数据的现有方法在AP任务中表现不佳，仅关注即时异常或未能为未来异常提供精确预测。为解决AP任务，我们提出名为Anomaly to Prompt (A2P)的新框架，包含Anomaly-Aware Forecasting (AAF)与Synthetic Anomaly Prompting (SAP)。为使预测模型能预测异常时间点，我们采用学习异常关系的策略。为稳健检测异常，我们提出的SAP引入了可学习的Anomaly Prompt Pool (APP)，利用信号自适应提示模拟多样异常模式。在多个真实世界数据集上的综合实验证明了A2P优于最先进方法，展示了其预测未来异常的能力。代码见https://github.com/KU-VGI/AP。
英文摘要：
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.
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。
