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

2026-04-02

AI Summary

This work addresses the limited performance and reproducibility of Matrix Profile–based time series anomaly detection on the TSB-AD benchmark. To overcome these issues, the authors propose an optimized approach that reduces computational complexity through pre-sorted multidimensional aggregation, suppresses redundant anomaly detections via exclusion-zone-aware k-nearest neighbor retrieval, and enhances localization accuracy with a moving-average post-processing step. The method achieves state-of-the-art results on both univariate and multivariate tracks of the TSB-AD benchmark. Furthermore, the authors release a complete open-source implementation along with detailed hyperparameter configurations, substantially improving result reproducibility and establishing a reliable baseline for the research community.

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.

Problem

• Matrix Profile
• time-series anomaly detection
• benchmark
• TSB-AD
• reproducibility

Innovation

• Matrix Profile
• anomaly detection
• exclusion-zone-aware kNN
• multivariate time series
• reproducible benchmark