Time series forecasting based on parallel neural network

J. M. Górriz, C. G. Puntonet, M. Salmerón, J. Ortega, M. Aldasht

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