# Neural Granger Causality for Nonlinear Time Series

@article{Tank2018NeuralGC, title={Neural Granger Causality for Nonlinear Time Series}, author={Alex Tank and Ian Covert and Nicholas J. Foti and Ali Shojaie and Emily B. Fox}, journal={arXiv: Machine Learning}, year={2018} }

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in applied domains, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific…

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