Discovering Nonlinear Relations with Minimum Predictive Information Regularization

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

Cited by in corpus (4)

• Interpretable Models for Granger Causality Using Self-explaining Neural Networks
• Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks
• Learning interaction rules from multi-animal trajectories via augmented behavioral models
• Discovering long term dependencies in noisy time series data using deep learning