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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In this paper, we propose a Bayesian framework for estimation of parameters of a mixture of autoregressive 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，并为真实的后验密度提供了易于处理的近似值，从而使Kullback-Liebler（KL）相对于先验分布的散度最小。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">(1);

Department of Computer Science and Engineering Indian Institute of Technology Madras">(1);

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原文格式 PDF
正文语种 eng
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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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