000108292 001__ 108292
000108292 005__ 20230519145346.0
000108292 0247_ $$2doi$$a10.1016/j.ijforecast.2020.10.004
000108292 0248_ $$2sideral$$a121103
000108292 037__ $$aART-2021-121103
000108292 041__ $$aeng
000108292 100__ $$aCalvo-Pardo, H.
000108292 245__ $$aGranger causality detection in high-dimensional systems using feedforward neural networks
000108292 260__ $$c2021
000108292 5060_ $$aAccess copy available to the general public$$fUnrestricted
000108292 5203_ $$aThis paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between 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 in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase 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.
000108292 536__ $$9info:eu-repo/grantAgreement/ES/DGA/ARAID
000108292 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000108292 590__ $$a7.022$$b2021
000108292 591__ $$aMANAGEMENT$$b57 / 228 = 0.25$$c2021$$dQ1$$eT1
000108292 591__ $$aECONOMICS$$b24 / 382 = 0.063$$c2021$$dQ1$$eT1
000108292 594__ $$a7.9$$b2021
000108292 592__ $$a1.99$$b2021
000108292 593__ $$aBusiness and International Management$$c2021$$dQ1
000108292 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000108292 700__ $$aMancini, T.
000108292 700__ $$0(orcid)0000-0002-0437-7812$$aOlmo, J.
000108292 773__ $$g37, 2 (2021), 920-940$$pInt. j. forecast.$$tINTERNATIONAL JOURNAL OF FORECASTING$$x0169-2070
000108292 8564_ $$s640509$$uhttps://zaguan.unizar.es/record/108292/files/texto_completo.pdf$$yPostprint
000108292 8564_ $$s2294961$$uhttps://zaguan.unizar.es/record/108292/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000108292 909CO $$ooai:zaguan.unizar.es:108292$$particulos$$pdriver
000108292 951__ $$a2023-05-18-13:21:55
000108292 980__ $$aARTICLE