Dynamic factor graphs for time series modeling

Piotr Mirowski, Yann LeCun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings
Pages128-143
Number of pages16
Volume5782 LNAI
EditionPART 2
DOIs
StatePublished - 2009
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009 - Bled, Slovenia
Duration: Sep 7 2009Sep 11 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5782 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009
CountrySlovenia
CityBled
Period9/7/099/11/09

Fingerprint

Factor Graph
Dynamic Graphs
Time Series Modelling
Time series
Energy
Joint Modeling
Hidden Variables
Electron energy levels
Time Series Prediction
Motion Capture
Constant function
Data acquisition
Chaotic Attractor
Partition Function
Assign
Smoothing
Update
Scalar
Benchmark
Gradient

Keywords

  • Dynamic Bayesian networks
  • Expectation-maximization
  • Factor graphs
  • Recurrent networks
  • Time series

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Mirowski, P., & LeCun, Y. (2009). Dynamic factor graphs for time series modeling. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings (PART 2 ed., Vol. 5782 LNAI, pp. 128-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5782 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-04174-7_9

Dynamic factor graphs for time series modeling. / Mirowski, Piotr; LeCun, Yann.

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings. Vol. 5782 LNAI PART 2. ed. 2009. p. 128-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5782 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mirowski, P & LeCun, Y 2009, Dynamic factor graphs for time series modeling. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings. PART 2 edn, vol. 5782 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 5782 LNAI, pp. 128-143, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009, Bled, Slovenia, 9/7/09. https://doi.org/10.1007/978-3-642-04174-7_9
Mirowski P, LeCun Y. Dynamic factor graphs for time series modeling. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings. PART 2 ed. Vol. 5782 LNAI. 2009. p. 128-143. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-04174-7_9
Mirowski, Piotr ; LeCun, Yann. / Dynamic factor graphs for time series modeling. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings. Vol. 5782 LNAI PART 2. ed. 2009. pp. 128-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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