Nonparametric Bayesian factor analysis of multiple time series
Publication
, Journal Article
Ray, P; Carin, L
Published in: IEEE Workshop on Statistical Signal Processing Proceedings
September 5, 2011
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.
Duke Scholars
Published In
IEEE Workshop on Statistical Signal Processing Proceedings
DOI
Publication Date
September 5, 2011
Start / End Page
49 / 52
Citation
APA
Chicago
ICMJE
MLA
NLM
Ray, P., & Carin, L. (2011). Nonparametric Bayesian factor analysis of multiple time series. IEEE Workshop on Statistical Signal Processing Proceedings, 49–52. https://doi.org/10.1109/SSP.2011.5967742
Ray, P., and L. Carin. “Nonparametric Bayesian factor analysis of multiple time series.” IEEE Workshop on Statistical Signal Processing Proceedings, September 5, 2011, 49–52. https://doi.org/10.1109/SSP.2011.5967742.
Ray P, Carin L. Nonparametric Bayesian factor analysis of multiple time series. IEEE Workshop on Statistical Signal Processing Proceedings. 2011 Sep 5;49–52.
Ray, P., and L. Carin. “Nonparametric Bayesian factor analysis of multiple time series.” IEEE Workshop on Statistical Signal Processing Proceedings, Sept. 2011, pp. 49–52. Scopus, doi:10.1109/SSP.2011.5967742.
Ray P, Carin L. Nonparametric Bayesian factor analysis of multiple time series. IEEE Workshop on Statistical Signal Processing Proceedings. 2011 Sep 5;49–52.
Published In
IEEE Workshop on Statistical Signal Processing Proceedings
DOI
Publication Date
September 5, 2011
Start / End Page
49 / 52