Feb 16, 2018 · We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.

We have presented a framework for nonlinear Granger causality selection using regularized neural network models of time series. To disentangle the effects of ...

The Neural-GC repository contains code for a deep learning-based approach to discovering Granger causality networks in multivariate time series. The methods ...

We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data ...

Aug 7, 2022 · This decoupling acts as a filtering and can be extended to any DL model including Multi-Layer Perceptrons (MLP), Recurrent Neural Networks (RNN) ...

It allows performing causality tests using neural networks based on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), or Multilayer Perceptron (MLP).

Thanks to its interpretability and absence of experimental data, Granger causality has become one of the most powerful tools in causal discovery.

This work proposes a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with ...

Feb 15, 2023 · Granger causality is a commonly used method for uncovering information flow and dependencies in a time series.

Granger causality has been widely used in various application domains to capture lead-lag relationships amongst the components of complex dynamical systems, ...