Dynamic Factor Graphs for Time Series Modeling

  • Piotr Mirowski
  • Yann LeCun
Conference paper

DOI: 10.1007/978-3-642-04174-7_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5782)
Cite this paper as:
Mirowski P., LeCun Y. (2009) Dynamic Factor Graphs for Time Series Modeling. In: Buntine W., Grobelnik M., Mladenić D., Shawe-Taylor J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science, vol 5782. Springer, Berlin, Heidelberg

Abstract

This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden variables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Because the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors’ parameters. These alternated inference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also successfully reconstruct missing motion capture data.

Keywords

factor graphs time series dynamic Bayesian networks recurrent networks expectation-maximization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Piotr Mirowski
    • 1
  • Yann LeCun
    • 1
  1. 1.Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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