TY - GEN
T1 - Nonparametric Bayesian factor analysis of multiple time series
AU - Ray, Priyadip
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/9/5
Y1 - 2011/9/5
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/5967742/
UR - http://www.scopus.com/inward/record.url?scp=80052217079&partnerID=8YFLogxK
U2 - 10.1109/SSP.2011.5967742
DO - 10.1109/SSP.2011.5967742
M3 - Conference contribution
SN - 9781457705700
SP - 49
EP - 52
BT - IEEE Workshop on Statistical Signal Processing Proceedings
ER -