Martha White, Junfeng Wen, Michael Bowling, and Dale Schuurmans. Optimal Estimation of Multivariate ARMA Models. In Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI), 2015. To Appear
Autoregressive moving average (ARMA) models are a fundamental tool in time series analysis that offer intuitive modeling capability and efficient predictors. Unfortunately, the lack of globally optimal parameter estimation strategies for these models remains a problem: application studies often adopt the simpler autoregressive model that can be easily estimated by maximizing (a posteriori) likelihood. We develop a (regularized, imputed) maximum likelihood criterion that admits efficient global estimation via structured matrix norm optimization methods. An empirical evaluation demonstrates the benefits of globally optimal parameter estimation over local and moment matching approaches.
@InProceedings(15aaai-arma, Title = "Optimal Estimation of Multivariate ARMA Models", Author = "Martha White and Junfeng Wen and Michael Bowling and Dale Schuurmans", Booktitle = "Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI)", Year = "2015", Note = "To Appear", AcceptRate = "27%", AcceptNumbers = "531 of 1991" )