基于矩阵轮廓的可复现异常检测系统，支持单变量与多变量时间序列。
Matrix Profile for Time-Series Anomaly Detection: A Reproducible Open-Source Benchmark on TSB-AD
- 融合预排序多维聚合与带排除区的k近邻检索，提升异常检测效率。
- 在TSB-AD基准上实现高精度，对重复异常有良好识别能力。
- 开源完整代码与超参设置，适合研究者复现与对比实验。
矩阵轮廓（Matrix Profile, MP）方法是一类可解释且可扩展的距离基础型时间序列异常检测方法，但高性能仍依赖于超出基础最近邻轮廓的设计选择。本技术报告详述了提交至TSB-AD基准的开放源码矩阵轮廓异常检测系统（MMPAD），该基准涵盖单变量与多变量时间序列。所提系统结合了预排序多维聚合、考虑排除区的高效k近邻（kNN）检索以处理重复异常，以及移动平均后处理机制。为提供基于MP的异常检测在TSB-AD上的可复现参考，本文详细公开了实现代码、单变量与多变量赛道的超参数配置及对应基准结果，并分析了系统在总体排行榜及不同数据集特性下的表现。开源代码已发布于https://github.com/mcyeh/mmpad_tsb。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。