Benchmarking Inductive Biases for Multivariate Time-Series Anomaly Detection with a Robust Multi-View Channel-Graph Detector
arXiv:2605.28103v1 Announce Type: new Abstract: We present a unified experiment, analysis, and benchmark study of multivariate time-series (MTS) anomaly detection. Ten family-representative detectors -- spanning statistical, reconstruction, association, frequency, and generic-transformer families -- are evaluated on five datasets (SMD, MSL, SMAP, PSM, and MSDS) under effectiveness, efficiency, robustness, and cross-dataset generalisation. All methods share the same windowing, scoring, hardware, and metric protocols. Effectiveness, ablation, and robustness use three random seeds; cross-dataset
The proliferation of complex AI systems, especially those operating autonomously, necessitates robust anomaly detection to ensure reliability, security, and performance. As AI's role expands, the need for standardized benchmarking in critical areas like time-series anomaly detection becomes paramount.
This research provides a foundational benchmark for multivariate time-series anomaly detection, which is critical for monitoring systems in fields ranging from industrial IoT to financial markets and autonomous agents. Standardized evaluation allows for better selection and development of AI models for real-world applications.
The unified experiment and benchmark study provide a clearer understanding of the strengths and weaknesses of various anomaly detection methods across diverse datasets, enabling more informed choices for AI system developers. It shifts the focus towards more robust and generalizable solutions for critical AI applications.
- · AI developers
- · Autonomous system operators
- · Industrial IoT sector
- · Cybersecurity researchers
- · Developers of un-robust anomaly detection methods
- · Systems relying on ad-hoc anomaly detection
Improved reliability and security of AI-driven systems due to better anomaly detection capabilities.
Accelerated development of next-generation AI agents and autonomous systems that can self-monitor and detect critical failures.
Enhanced trust in AI systems, potentially leading to faster adoption in high-stakes environments like critical infrastructure or defence applications.
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Read at arXiv cs.LG