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05 May 2021
Conference Paper
Titel

Switching Dynamical Systems with Deep Neural Networks

Abstract
The problem of uncovering different dynamical regimes is of pivotal importance in time series analysis. Switching dynamical systems provide a solution for modeling physical phenomena whose time series data exhibit different dynamical modes. In this work we propose a novel variational RNN model for switching dynamics allowing for both non-Markovian and nonlinear dynamical behavior between and within dynamic modes. Attention mechanisms are provided to inform the switching distribution. We evaluate our model on synthetic and empirical datasets of diverse nature and successfully uncover different dynamical regimes and predict the switching dynamics.
Author(s)
Ojeda, César
TU Berlin
Georgiev, Bogdan
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Cvejoski, Kostadin
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Schücker, Jannis
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Bauckhage, Christian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Sánchez, Ramsés J.
Uni Bonn
Hauptwerk
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Konferenz
International Conference on Pattern Recognition (ICPR) 2021
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DOI
10.1109/ICPR48806.2021.9412566
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Tags
  • time series analysis

  • neural networks

  • switches

  • predictive models

  • data models

  • pattern recognition

  • dynamical system

  • Neural Nets

  • nonlinear dynamical systems

  • recurrent neural networks

  • time series

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