Matrix Time Series Models

• Activity Number: 498
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Seyed Yaser Samadi*+ and Lynne Billard
• Companies: University of Georgia and University of Georgia
• Keywords: time series ; matrix variate ; cross-autocorrelation

Abstract

Many data sets in many scientific areas deal with multiple sets of multivariate time series. While single univariate and single vector time series are well developed in the literature, multiple sets of multivariate time series have not yet been studied. Therefore, a class of matrix time series models is introduced for dealing with the situation where there are multiple sets of multivariate time series data. Explicit expressions for a matrix autoregressive model of order p along with its cross-autocorrelation functions are derived. Parameters of the proposed matrix time series model are estimated by ordinary and generalized least square method, maximum likelihood estimation, and Bayesian methods.