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Scalable probabilistic modeling and prediction in high dimensionalmultivariate time-series is a challenging problem, particularly for systemswith hidden sources of dependence and/or homogeneity. Examples of such problemsinclude dynamic social networks with co-evolving nodes and edges and dynamicstudent learning in online courses. Here, we address these problems through thediscovery of hierarchical latent groups. We introduce a family of ConditionalLatent Tree Models (CLTM), in which tree-structured latent variablesincorporate the unknown groups. The latent tree itself is conditioned onobserved covariates such as seasonality, historical activity, and nodeattributes. We propose a statistically efficient framework for learning boththe hierarchical tree structure and the parameters of the CLTM. We demonstratecompetitive performance in multiple real world datasets from different domains.These include a dataset on students' attempts at answering questions in apsychology MOOC, Twitter users participating in an emergency managementdiscussion and interacting with one another, and windsurfers interacting on abeach in Southern California. In addition, our modeling framework providesvaluable and interpretable information about the hidden group structures andtheir effect on the evolution of the time series.

Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]