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Time series forecasting based on parallel neural network

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dc.contributor.author J. M. Górriz, C. G. Puntonet, M. Salmerón, J. Ortega, M. Aldasht
dc.date.accessioned 2018-03-13T08:32:32Z
dc.date.accessioned 2022-05-22T08:28:49Z
dc.date.available 2018-03-13T08:32:32Z
dc.date.available 2022-05-22T08:28:49Z
dc.date.issued 2004-02-29
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/7977
dc.description.abstract In this paper we show a Parallel Neural Network (Cross-over Prediction Model) for time series forecasting implemented in PVM (”Parallel Virtual Machine”) and MPI (”Message Passing Interface”), in order to reduce computational time. Parallelization is achieved twofold: (a) updating autoregressive parameters using a genetic algorithm (GA) and (b) evaluating the overall prediction function via a parallel neural network. We implement the GA in two popular architectures of parallel processors (i.e hypercube and 2D-mesh) and discuss their time efficiency. en_US
dc.language.iso en en_US
dc.publisher IEEE EIS-2004, 29-febrero al 2 de Marzo, Madeira-Portugal. en_US
dc.subject Artificial Neural Networks (ANNS), Auto-Regressive Models (AR), Parallel Virtual Machine (PVM), Array and Hypercube Networks, Mask Functions, Quicksort, Genetic Algorithms en_US
dc.title Time series forecasting based on parallel neural network en_US
dc.type Article en_US

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