Work

Learning Temporal Dependence from Time-Series Data with Latent Variables

Year: 2016

Type: conference-paper

Abstract: We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes. A special case of wide interest and applicability is the setting where the noise is Gaussian and relationships are Markov and linear. We study this setting with two additional features: firstly, each random process has a hidden (latent) state, which we use to model the internal memory possessed by the variables (similar to hidden Markov models). Secondly, each variable can depend on its latent memory state through a random lag (rather than a fixed lag), thus modeling memory recall with differing lags at distinct times. Under this setting, we develop an estimator and prove that under a genericity assumption, the parameters of the model can be learned consistently. We also propose a practical adaption of this estimator, which demonstrates significant performance gains in both synthetic and real-world datasets.

Source: IEEE International Conference on Data Science and Advanced Analytics (DSAA)

DOI: 10.1109/dsaa.2016.34

Authors: Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

Institution: University of Washington

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Office of Naval Research

Awards: G4612476680, G8876996369, G8882160369