Nonparametric Bayesian factor analysis of multiple time series

Priyadip Ray, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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. © 2011 IEEE.
Original languageEnglish (US)
Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
Pages49-52
Number of pages4
DOIs
StatePublished - Sep 5 2011
Externally publishedYes

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