Granger Causality Analysis based on Neural Networks Architectures for bivariate cases

Alvaro D. Orjuela-Canon, Jan A. Freund, Andres Jutinico, Alexander Cerquera

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work deals with an analysis of Granger Causality computation based on artificial neural networks, including a nonlinear relation between the involved variables. Information about the training parameters are exhibited in order to visualize how the conditions of the chosen model to obtain the connectivity information depend on the architecture of network. Three chaotic maps with a bivariate case built from two time series were employed to see the effect of training parameters of the models. Nonlinear autoregressive and nonlinear autoregressive with exogenous inputs were used to forecast the time series, and then, obtain the causality information based on differences of errors between both approximations. Results show that the causality computation is sensible to neural network parameters previously untreated in a detailed mode.

Original languageEnglish (US)
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
DOIs
StatePublished - Jul 24 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: Jul 19 2020Jul 24 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period7/19/207/24/20

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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