Discovering Nonlinear Relations with Minimum Predictive Information Regularization
arXiv:2001.01885
Abstract
Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications, yet remains a hard problem. In this work, we introduce a novel minimum predictive information regularization method to infer directional relations from time series, allowing deep learning models to discover nonlinear relations. Our method substantially outperforms other methods for learning nonlinear relations in synthetic datasets, and discovers the directional relations in a video game environment and a heart-rate vs. breath-rate dataset.
26 pages, 11 figures; ICML'19 Time Series Workshop
References in corpus (7)
- Interaction Networks for Learning about Objects, Relations and Physics
- Group Sparse Regularization for Deep Neural Networks
- Kernel method for nonlinear Granger causality
- Local information transfer as a spatiotemporal filter for complex systems
- Kernel Granger causality and the analysis of dynamical networks
- Permutation-equivariant neural networks applied to dynamics prediction
- Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality