通过不确定性感知的时间序列集成预测异常前兆
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
AI总结：
FATE通过不确定性感知的时间序列集成方法，有效检测异常前兆，无需异常标签，在多个数据集上取得显著提升。
摘要：
在工业运营、金融和网络安全等领域，检测时间序列数据中的异常至关重要，因为早期识别异常模式对于确保系统可靠性和实现预防性维护至关重要。然而，大多数现有方法是反应性的：它们只在异常发生后检测异常，缺乏提供主动早期预警信号的能力。在本文中，我们提出了FATE（通过时间序列集成预测异常，PoA），一种新的无监督框架，通过从多样化的时序预测模型中量化预测不确定性来检测异常前兆（PoA）。与以往依赖重建误差或需要真实标签的方法不同，FATE预测未来值，并利用集成中的分歧来信号潜在异常的早期迹象，而无需在推理时访问目标值。为了严格评估PoA检测，我们引入了Precursor Time-series Aware Precision and Recall（PTaPR），一种新的指标，通过联合评估段级准确性、段内覆盖和时间紧迫性来扩展传统的Time-series Aware Precision and Recall（TaPR）。这使能够更全面地评估现有指标忽视的早期预警能力。在五个真实世界基准数据集上的实验表明，FATE在PTaPR AUC上平均提高了19.9个百分点，在早期检测F1分数上提高了20.02个百分点，优于基线方法，且无需异常标签。这些结果证明了FATE在复杂时间序列环境中的有效性和实用性。
英文摘要：
Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essential for ensuring system reliability and enabling preventive maintenance. However, most existing methods are reactive: they detect anomalies only after they occur and lack the capability to provide proactive early warning signals. In this paper, we propose FATE (Forecasting Anomalies with Time-series Ensembles), a novel unsupervised framework for detecting Precursors-of-Anomaly (PoA) by quantifying predictive uncertainty from a diverse ensemble of time-series forecasting models. Unlike prior approaches that rely on reconstruction errors or require ground-truth labels, FATE anticipates future values and leverages ensemble disagreement to signal early signs of potential anomalies without access to target values at inference time. To rigorously evaluate PoA detection, we introduce Precursor Time-series Aware Precision and Recall (PTaPR), a new metric that extends the traditional Time-series Aware Precision and Recall (TaPR) by jointly assessing segment-level accuracy, within-segment coverage, and temporal promptness of early predictions. This enables a more holistic assessment of early warning capabilities that existing metrics overlook. Experiments on five real-world benchmark datasets show that FATE achieves an average improvement of 19.9 percentage points in PTaPR AUC and 20.02 percentage points in early detection F1 score, outperforming baselines while requiring no anomaly labels. These results demonstrate the effectiveness and practicality of FATE for real-time unsupervised early warning in complex time-series environments.
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。
