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.
Switching Dynamical Systems with Deep Neural Networks
Type: Inproceedings
Author: C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez
Journal: 2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021
Citation information
C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez:
Switching Dynamical Systems with Deep Neural Networks.
2020 25th International Conference on Pattern Recognition (ICPR),
2021,
6305-6312,
IEEE,
Milan, Italy,
https://doi.org/10.1109/ICPR48806.2021.9412566