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[2602.17028] Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Summary
The paper presents FATE, an innovative framework for forecasting anomaly precursors in time-series data using uncertainty-aware ensembles, enhancing proactive anomaly detection.
Why It Matters
This research addresses a critical gap in anomaly detection by enabling proactive identification of potential issues in various domains, such as finance and cybersecurity. By utilizing an unsupervised approach, it allows for early warnings without needing labeled data, which is often scarce. The introduction of a new evaluation metric (PTaPR) further enhances the assessment of early detection capabilities, making it a significant contribution to the field of machine learning.
Key Takeaways
- FATE framework forecasts anomalies using ensemble models.
- It quantifies predictive uncertainty to signal potential anomalies.
- Introduces a new metric (PTaPR) for evaluating early detection.
- Achieves significant improvements in detection performance on benchmark datasets.
- Operates without the need for labeled anomaly data.
Computer Science > Machine Learning arXiv:2602.17028 (cs) [Submitted on 19 Feb 2026] Title:Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles Authors:Hyeongwon Kang, Jinwoo Park, Seunghun Han, Pilsung Kang View a PDF of the paper titled Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles, by Hyeongwon Kang and 2 other authors View PDF HTML (experimental) Abstract: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...
