RED-F: 基于重建-消除的双流对比预测框架用于多变量时间序列异常预测
RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction
AI总结：
RED-F通过重建-消除模型和双流对比预测模型，提升多变量时间序列异常预测的准确性与鲁棒性。
摘要：
多变量时间序列（MTS）中的异常预测（AP）对于确保系统可靠性至关重要。现有方法要么只关注是否即将出现异常，而没有提供对未来异常的精确预测，要么直接在历史数据上进行预测，这很容易被正常模式淹没。为了解决AP任务中的挑战，我们提出RED-F，一种由重建-消除模型（REM）和双流对比预测模型（DFM）组成的新型框架。我们利用REM从历史数据中构建正常模式的基础，为后续的异常预测提供基础。然后DFM同时预测构建的正常模式和当前窗口，采用对比预测，通过计算这两个预测之间的差异，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。为了使预测模型能够生成不易被正常模式掩盖的预测，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。在多个真实世界数据集上的广泛实验表明，RED-F在异常预测任务中具有优越的能力。我们的代码可在http://github.com/PenyChen/RED-F获取。
英文摘要：
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.
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