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Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series

Sergey Kirshner; Padhraic Smyth; Andrew W. Robertson

Title:
Conditional Chow-Liu Tree Structures for Modeling Discrete-Valued Vector Time Series
Author(s):
Kirshner, Sergey
Smyth, Padhraic
Robertson, Andrew W.
Date:
Type:
Technical reports
Department:
International Research Institute for Climate and Society
Permanent URL:
Part Number:
04-04
Publisher:
School of Information and Computer Science, University of California, Irvine
Publisher Location:
Irvine, Calif.
Abstract:
We consider the problem of modeling discrete-valued vector time series data using extensions of Chow-Liu tree models to capture both dependencies across time and dependencies across variables. We introduce conditional Chow-Liu tree models, an extension to standard Chow-Liu trees, for modeling conditional rather than joint densities. We describe learning algorithms for such models and show how they can be used to learn parsimonious representations for the output distributions in hidden Markov models. We illustrate how these models can be applied to the important problem of simulating and forecasting daily precipitation occurrence for networks of rain stations. To illustrate the effectiveness of the models, we compare their performance versus a number of alternatives using historical precipitation data from Southwestern Australia and the Western United States. We illustrate how the structure and parameters of the models can be used to provide an improved meteorological interpretation of such data.
Subject(s):
Meteorology
Item views:
78
Metadata:
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