Bayesian mixture of AR models for time series clustering

B. Venkataramana Kini; C. Chandra Sekhar

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Bayesian mixture of AR models for time series clustering

机译：时间序列聚类的AR模型的贝叶斯混合

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摘要

In this paper, we propose a Bayesian framework for estimation of parameters of a mixture of autore-gressive models for time series clustering. The proposed approach is based on variational principles and provides a tractable approximation to the true posterior density that minimizes Kullback-Liebler (KL) divergence with respect to prior distribution. This method simultaneously addresses the model complexity and parameter estimation problems. The proposed approach is applied both on simulated and real-world time series datasets. It is found to be useful in exploring and finding the true number of underlying clusters, starting from an arbitrarily large number of clusters.

机译：在本文中，我们提出了一种贝叶斯框架，用于估计时间序列聚类的自回归模型的混合参数。所提出的方法基于变分原理，并且提供了对真实后验密度的易于处理的近似，从而相对于先前的分布最小化了Kullback-Liebler（KL）散度。该方法同时解决了模型复杂性和参数估计问题。所提出的方法可应用于模拟和实时时间序列数据集。从任意数量的群集开始，发现它对于探索和查找实际数量的基础群集很有用。

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来源
《Pattern Analysis and Applications》 |2013年第2期|179-200|共22页
作者
B. Venkataramana Kini; C. Chandra Sekhar;
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作者单位

Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600036, India;

Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600036, India;

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收录信息美国《科学引文索引》(SCI);
原文格式 PDF
正文语种 eng
中图分类
关键词
time series clustering; mixture of autoregressive models; bayesian estimation; variational bayesian expectation maximization method; automatic model complexity determination;

机译：时间序列聚类;自回归模型的混合;贝叶斯估计变分贝叶斯期望最大化方法自动模型复杂度确定;

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4. Bayesian Mixture of AR Models for Time Series Clustering [C] . B Venkataramana Kini, Sekhar C. Chandra International Conference on Advances in Pattern Recognition . 2008

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Bayesian mixture of AR models for time series clustering

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