标题： 贝叶斯非参数动态聚类时间序列 显示英文标题

标题： Bayesian Nonparametric Dynamical Clustering of Time Series

摘要： 我们提出了一种方法，通过在具有线性动态的未知数量的状态之间切换来建模无限数量的时间序列聚类的演变。 我们开发了一种贝叶斯非参数方法，使用分层狄利克雷过程作为切换线性动态系统参数的先验，并使用高斯过程先验来建模每个聚类中幅度和时间对齐的统计变化。 通过建模时间序列模式的演变，该方法以合理的方式避免了聚类的不必要的扩散。 我们通过为离线和在线场景制定变分下界来进行推断，通过优化实现高效的学习。 我们通过几个使用公开可用数据库的心电图分析案例研究展示了该方法的通用性和有效性。 显示英文摘要

摘要： 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.