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2010
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Learning Temporal Causal Graphs for Relational Time-Series Analysis

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Learning Temporal Causal Graphs for Relational Time-Series Analysis
Learning temporal causal graph structures from multivariate time-series data reveals important dependency relationships between current observations and histories, and provides a better understanding of complex systems. In this paper, we examine learning tasks where one is presented with multiple multivariate time-series, as well as a relational graph among the different time-series. We propose an L1 regularized hidden Markov random field regression framework to leverage the information provided by the relational graph and jointly infer more accurate temporal causal structures for all time-series. We test the proposed model on climate modeling and cross-species microarray data analysis applications.
Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C. Lozano, Yong Lu
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