arXiv cs.AI· Adri\'an P\'erez-Herrero, Paulo F\'elix, Jes\'us Presedo, Carl Henrik Ek·· 12 小时前AI 评分18
基于贝叶斯非参数方法的时间序列动态聚类
Bayesian Nonparametric Dynamical Clustering of Time Series
AI 导读
研究提出一种贝叶斯非参数方法，用分层 Dirichlet 过程作为 Switching Linear Dynamical System 参数的先验，并以高斯过程先验建模各聚类内幅度与时间对齐的统计变化，从而在未知数量线性动态机制间切换、跟踪无界数量时间序列聚类的演化。该方法通过变分下界统一处理离线与在线场景，以优化方式高效学习，并在公开数据库的心电图分析案例中验证了有效性。
正文 · 原文
Abstract:We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the parameters of a Switching Linear Dynamical System and a Gaussian process prior to model the statistical variations in amplitude and temporal alignment within each cluster. By modeling the evolution of time series patterns, the method avoids unnecessary proliferation of clusters in a principled manner. We perform inference by formulating a variational lower bound for off-line and on-line scenarios, enabling efficient learning through optimization. We illustrate the versatility and effectiveness of the approach through several case studies of electrocardiogram analysis using publicly available databases.
| Comments: | Revised version with methodological refinements, expanded experimental results, and improvements to the presentation |
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Applications (stat.AP) |
| ACM classes: | I.5; I.2.1 |
| Cite as: | arXiv:2510.06919 [stat.ML] |
| (or arXiv:2510.06919v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2510.06919 arXiv-issued DOI via DataCite |
Submission history
From: Adrian Perez-Herrero [view email]
[v1]
Wed, 8 Oct 2025 11:52:39 UTC (5,541 KB)
[v2]
Tue, 20 Oct 2026 17:41:05 UTC (13,408 KB)
来源：arXiv cs.AI · arxiv.org