

























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 this http URL open-source implementation is available at this https URL.
| Comments: | this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.02445 [cs.LG] |
| (or arXiv:2604.02445v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.02445 arXiv-issued DOI via DataCite |
From: Chin-Chia Michael Yeh [view email]
[v1]
Thu, 2 Apr 2026 18:16:33 UTC (1,835 KB)
[v2]
Tue, 7 Apr 2026 21:58:49 UTC (1,835 KB)
[v3]
Fri, 24 Apr 2026 22:18:49 UTC (1,835 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。