Anomaly prediction (AP) in multivariate time series (MTS) is crucial to ensure system dependability. Existing methods either focus solely on whether an anomaly is imminent without providing precise predictions for the future anomaly, or performing predictions directly on historical data, which is easily drowned out by the normal patterns. To address the challenges in AP task, we propose RED-F, a novel framework comprised of the Reconstruction-Elimination Model (REM) and the Dual-stream Contrastive Forecasting Model (DFM). We utilize REM to construct a baseline of normal patterns from historical data, providing a foundation for subsequent predictions of anomalies. Then DFM simultaneously predicts both the constructed normal pattern and the current window, employing a contrastive forecast that transforms the difficult AP task into a simpler, more robust task of relative trajectory comparison by computing the divergence between these two predictions. To enable the forecasting model to generate a prediction not easily obscured by normal patterns, we propose a Multi-Series Prediction (MSP) training objective to enhance its sensitivity to the current window. Extensive experiments on multiple real-world datasets demonstrate the superior capability of RED-F in anomaly prediction tasks. Our code is available at http://github.com/PenyChen/RED-F.

翻译：多元时间序列（MTS）中的异常预测（AP）对于确保系统可靠性至关重要。现有方法要么仅关注异常是否即将发生而无法对未来异常提供精确预测，要么直接在历史数据上进行预测，其预测结果易被正常模式所淹没。为应对AP任务中的挑战，本文提出RED-F——一个由重构-消除模型（REM）与双流对比预测模型（DFM）构成的新型框架。我们利用REM从历史数据中构建正常模式的基线，为后续异常预测提供基础。随后，DFM同时预测构建的正常模式与当前时间窗口，通过计算两个预测之间的差异进行对比预测，从而将困难的AP任务转化为更简单、更稳健的相对轨迹比较任务。为使预测模型生成不易被正常模式掩盖的预测结果，我们提出多序列预测（MSP）训练目标以增强其对当前窗口的敏感性。在多个真实数据集上的大量实验表明，RED-F在异常预测任务中具有卓越性能。代码已开源：http://github.com/PenyChen/RED-F。