NONPARAMETRIC BAYESIAN FACTOR ANALYSIS OF MULTIPLE TIME SERIES

被引：0

作者：

Ray, Priyadip ^{[1
]}

Carin, Lawrence ^{[1
]}

机构：

[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA

来源：

2011 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2011年

关键词：

Nonparametric Bayesian Models; Pitman-Yor Process; Factor Analysis; Infinite Mixture Models; COMMON TRENDS;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose a nonparametric Bayesian factor analysis framework for characterization of multiple time-series. The proposed model automatically infers the number of factors and the noise/residual variance, and it is also able to cluster time series which behave similarly over prescribed time windows. We use a Pitman-Yor process to impose such clustering. We also provide a general MCMC inference scheme and demonstrate the proposed framework on the analysis of multi-year stock prices of companies in the S & P 500.

引用

页码：49 / 52

页数：4

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