Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
arXiv:2602.17028v1 Announce Type: new 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 detectio
arXiv:2602.17028v1 Announce Type: new 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 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.
Executive Summary
The article proposes FATE, a novel unsupervised framework for detecting anomaly precursors in time-series data. FATE leverages ensemble disagreement to signal early signs of potential anomalies without requiring ground-truth labels. The framework is evaluated using a new metric, Precursor Time-series Aware Precision and Recall (PTaPR), which assesses segment-level accuracy, within-segment coverage, and temporal promptness. Experiments on real-world datasets demonstrate FATE's effectiveness, achieving an average improvement of 19.9 percentage points in PTaPR AUC and 20.02 percentage points in early detection F1 score.
Key Points
- ▸ FATE is an unsupervised framework for detecting anomaly precursors in time-series data
- ▸ The framework leverages ensemble disagreement to signal early signs of potential anomalies
- ▸ FATE is evaluated using a new metric, Precursor Time-series Aware Precision and Recall (PTaPR)
Merits
Effective Anomaly Detection
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
Unsupervised Approach
FATE does not require ground-truth labels, making it a practical solution for real-time anomaly detection
Demerits
Limited Evaluation
The framework is evaluated on only five real-world benchmark datasets, which may not be representative of all possible scenarios
Complexity
FATE's ensemble-based approach may be computationally intensive and require significant resources
Expert Commentary
The proposed FATE framework offers a significant advancement in anomaly detection, particularly in its ability to provide early warning signals without requiring ground-truth labels. The use of ensemble disagreement to quantify predictive uncertainty is a novel approach that addresses the limitations of traditional methods. However, further evaluation on diverse datasets and scenarios is necessary to fully assess FATE's effectiveness and robustness. Additionally, the computational complexity of the framework may be a concern in resource-constrained environments.
Recommendations
- ✓ Further evaluation of FATE on diverse datasets and scenarios to assess its robustness and effectiveness
- ✓ Investigation of techniques to reduce the computational complexity of FATE, such as model pruning or knowledge distillation
