异常预测：基于未来上下文建模的时间序列异常预测
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
AI总结：
针对时间序列异常发生后再检测会导致损失的问题，提出未来上下文建模（FCM）方法，利用长期预测模型放大观测数据的细微异常差异并结合联合变量-时间注意力进行正常性建模，实现异常事件预警，在五个数据集上召回率达70%+且F1分数显著优于基线。
摘要：
从时间序列数据中识别异常在基础设施安全、智能运维和太空探索等各个领域发挥着重要作用。当前的研究侧重于在异常发生后进行检测，这可能导致重大的财务/声誉损失或基础设施损坏。在这项工作中，我们转而研究一个更实用但极具挑战性的问题，即时间序列异常预测，旨在为异常事件发生前提供早期预警。为了解决这个问题，我们引入了一种新颖且有原则的方法，即未来上下文建模（FCM）。其关键洞察在于，如果目标窗口中的未来异常事件的前置观测窗口与正常数据相比表现出任何细微差异，就可以准确预测这些事件。为了有效捕捉这种差异，FCM首先利用长期预测模型基于观测数据生成具有判别力的未来上下文，旨在放大那些细微但不寻常的差异。随后，它对观测数据与预测未来上下文的正常性相关性进行建模，以补充观测数据在预见目标窗口可能出现的异常时的正常性建模。FCM中还引入了联合变量-时间注意力学习，以同时利用时间序列数据的时间信号和特征，在上述两个视图中进行更具判别力的正常性建模。在五个数据集上的综合实验表明，FCM在多个数据集上获得了良好的召回率（70%+），并在F1分数上显著优于所有基线方法。代码可在https://github.com/mala-lab/FCM获取。
英文摘要：
Identifying anomalies from time series data plays an important role in various fields such as infrastructure security, intelligent operation and maintenance, and space exploration. Current research focuses on detecting the anomalies after they occur, which can lead to significant financial/reputation loss or infrastructure damage. In this work we instead study a more practical yet very challenging problem, time series anomaly prediction, aiming at providing early warnings for abnormal events before their occurrence. To tackle this problem, we introduce a novel principled approach, namely future context modeling (FCM). Its key insight is that the future abnormal events in a target window can be accurately predicted if their preceding observation window exhibits any subtle difference to normal data. To effectively capture such differences, FCM first leverages long-term forecasting models to generate a discriminative future context based on the observation data, aiming to amplify those subtle but unusual difference. It then models a normality correlation of the observation data with the forecasting future context to complement the normality modeling of the observation data in foreseeing possible abnormality in the target window. A joint variate-time attention learning is also introduced in FCM to leverage both temporal signals and features of the time series data for more discriminative normality modeling in the aforementioned two views. Comprehensive experiments on five datasets demonstrate that FCM gains good recall rate (70\%+) on multiple datasets and significantly outperforms all baselines in F1 score. Code is available at https://github.com/mala-lab/FCM.
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。
