Nonparametric Bayesian factor analysis of multiple time series


Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Ray, P; Carin, L

Published Date

  • September 5, 2011

Published In

  • Ieee Workshop on Statistical Signal Processing Proceedings

Start / End Page

  • 49 - 52

Digital Object Identifier (DOI)

  • 10.1109/SSP.2011.5967742

Citation Source

  • Scopus