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arXiv · 2410.22735

MIXAD: Memory-Induced Explainable Time Series Anomaly Detection

Minha Kim·Kishor Kumar Bhaumik·Amin Ahsan Ali·Simon S. Woo
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Abstract

For modern industrial applications, accurately detecting and diagnosing anomalies in multivariate time series data is essential. Despite such need, most state-of-the-art methods often prioritize detection performance over model interpretability. Addressing this gap, we introduce MIXAD (Memory-Induced Explainable Time Series Anomaly Detection), a model designed for interpretable anomaly detection. MIXAD leverages a memory network alongside spatiotemporal processing units to understand the intricate dynamics and topological structures inherent in sensor relationships. We also introduce a novel anomaly scoring method that detects significant shifts in memory activation patterns during anomalies. Our approach not only ensures decent detection performance but also outperforms state-of-the-art baselines by 34.30% and 34.51% in interpretability metrics.

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Minha Kim, Kishor Kumar Bhaumik, Amin Ahsan Ali, Simon S. Woo. 2024-10-30. MIXAD: Memory-Induced Explainable Time Series Anomaly Detection. https://arxiv.org/abs/2410.22735

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