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arXiv · 2609.23883

Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus

Diogo Risca·Afonso Lourenço·Ricardo Martins·Goreti Marreiros
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Abstract

We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision loop. The system combines heterogeneous detectors and aggregates their outputs through a normalization-based weighted consensus, complemented by artifact-aware rules to stabilize anomaly scoring under deployment. To improve interpretability, it derives surrogate models that approximate the ensemble consensus and expose human-readable sensor conditions associated with anomalous behavior. Analysts can actively intervene by reviewing anomaly episodes, adjusting consensus behavior, and refining surrogate rules used for anomaly prediction, producing a human-adjusted ensemble. We evaluate the approach on an industrial stream with 260\,000 events and 3 anomalous episodes, showing robust detection and actionable human-AI interaction.

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Diogo Risca, Afonso Lourenço, Ricardo Martins, Goreti Marreiros. 2026-09-20. Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus. https://arxiv.org/abs/2609.23883

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