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Granger causality detection in high-dimensional systems using feedforward neural networks

Granger causality detection in high-dimensional systems using feedforward neural networks
Granger causality detection in high-dimensional systems using feedforward neural networks
This paper proposes a novel methodology to detect Granger causality in mean in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between the elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure. First, we maximize the transfer of information between input and output variables in the network to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function to identify the variables that have predictive ability and, hence, Granger cause the others. The penalty function inducing sparsity is applied to the weights characterizing the nodes of the neural network. We show the correct identification of these weights for increasing sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.
Granger causality, Lasso penalty function, Mutual information, Neural networks, Sparsity
0169-2070
920-940
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Calvo-Pardo, Hector
07a586f0-48ec-4049-932e-fb9fc575f59f
Mancini, Tullio
3e5a59a2-e184-4996-a7d6-7b4394bec08c
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e

Calvo-Pardo, Hector, Mancini, Tullio and Olmo, Jose (2020) Granger causality detection in high-dimensional systems using feedforward neural networks. International Journal of Forecasting, 37 (2), 920-940. (doi:10.1016/j.ijforecast.2020.10.004).

Record type: Article

Abstract

This paper proposes a novel methodology to detect Granger causality in mean in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between the elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure. First, we maximize the transfer of information between input and output variables in the network to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function to identify the variables that have predictive ability and, hence, Granger cause the others. The penalty function inducing sparsity is applied to the weights characterizing the nodes of the neural network. We show the correct identification of these weights for increasing sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.

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CalvoManciniOlmoIJoFRevised_final - Accepted Manuscript
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Accepted/In Press date: 14 October 2020
e-pub ahead of print date: 14 November 2020
Keywords: Granger causality, Lasso penalty function, Mutual information, Neural networks, Sparsity

Identifiers

Local EPrints ID: 444615
URI: http://eprints.soton.ac.uk/id/eprint/444615
ISSN: 0169-2070
PURE UUID: 19799131-0f70-437e-a769-d12822ddd749
ORCID for Hector Calvo-Pardo: ORCID iD orcid.org/0000-0001-6645-4273
ORCID for Jose Olmo: ORCID iD orcid.org/0000-0002-0437-7812

Catalogue record

Date deposited: 27 Oct 2020 19:55
Last modified: 28 Apr 2022 02:09

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Contributors

Author: Tullio Mancini
Author: Jose Olmo ORCID iD

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