Matrix Profile for Time-Series Anomaly Detection: A Reproducible Open-Source Benchmark on TSB-AD

arXiv cs.LG / 4/6/2026

💬 OpinionTools & Practical UsageModels & Research

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Key Points

The report presents an open-source, reproducible Matrix Profile for Anomaly Detection (MMPAD) submission to the TSB-AD benchmark, targeting both univariate and multivariate time-series anomaly detection.
It shows that strong Matrix Profile benchmark performance depends on non-trivial design choices beyond a basic nearest-neighbor matrix profile approach.
The submitted system combines pre-sorted multidimensional aggregation, exclusion-zone-aware kNN retrieval tailored for repeated anomalies, and moving-average post-processing.
The authors release the implementation and provide detailed hyperparameter settings and benchmark results for the univariate and multivariate tracks.
They also analyze performance patterns on the aggregate leaderboard and relate outcomes to dataset characteristics to explain when the approach works best.

Abstract

Matrix Profile (MP) methods are an interpretable and scalable family of distance-based methods for time-series anomaly detection, but strong benchmark performance still depends on design choices beyond a vanilla nearest-neighbor profile. This technical report documents an open-source Matrix Profile for Anomaly Detection (MMPAD) submission to TSB-AD, a benchmark that covers both univariate and multivariate time series. The submitted system combines pre-sorted multidimensional aggregation, efficient exclusion-zone-aware k-nearest-neighbor (kNN) retrieval for repeated anomalies, and moving-average post-processing. To serve as a reproducible reference for MP-based anomaly detection on TSB-AD, we detail the released implementation, the hyperparameter settings for the univariate and multivariate tracks, and the corresponding benchmark results. We further analyze how the system performs on the aggregate leaderboard and across specific dataset characteristics.The open-source implementation is available at https://github.com/mcyeh/mmpad_tsb.