JSM 2005 Online Program

Abstract #302579

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	38
Type:	Invited
Date/Time:	Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor:	IMS
Abstract - #302579
Title:	ARMA Time-Series Modeling with Graphical Models
Author(s):	Bo Thiesson*+ and David M. Chickering and David Heckerman and Christopher Meek
Companies:	Microsoft Research and Microsoft Research and Microsoft Research and Microsoft Research
Address:	One Microsoft Way, Redmond, WA, 98052,
Keywords:	Time series ; Graphical models ; Learning ; Missing data ; Smoothing ; Cross predictors
Abstract:	We express the classic ARMA time-series model as a directed graphical model. In doing so, we find the deterministic relationships in the model make it effectively impossible to use the EM algorithm for learning model parameters. To remedy this problem, we replace the deterministic relationships with Gaussian distributions having a small variance, yielding the stochastic ARMA model. This modification allows us to use the EM algorithm to learn parameters and to forecast, even in situations where some data is missing. This modification, in conjunction with the graphical model approach, also allows us to include cross predictors in situations where there are multiple time series and/or additional nontemporal covariates. More surprising, experiments suggest the move to stochastic ARMA yields improved accuracy through better smoothing. We demonstrate improvements afforded by cross prediction and better smoothing on real data.

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