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.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Yan Liu 0002, Alexandru Niculescu-Mizil, Aurelie C. Lozano, Yong Lu
• Keywords: ICML 2010, Machine Learning, Multiple Multivariate Time-series, Multivariate Time-series, Multivariate Time-series Data