Parallelization of Time Series Forecasting Model

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	Similar Articles Articles by J. M. Górriz Articles by Carlos G. Puntonet Articles by Moisés Salmerón Articles by R. Martin-Clemente

12th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP'04)

A Coruna, Spain

February 11-February 13

ISBN: 0-7695-2083-9

	ASCII Text	x
	J. M. Górriz, Carlos G. Puntonet, Moisés Salmerón, R. Martin-Clemente, "Parallelization of Time Series Forecasting Model," Parallel, Distributed, and Network-Based Processing, Euromicro Conference on, pp. 103, 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP'04), 2004.

	BibTex	x
	@article{ 10.1109/EMPDP.2004.1271434, author = {J. M. Górriz and Carlos G. Puntonet and Moisés Salmerón and R. Martin-Clemente}, title = {Parallelization of Time Series Forecasting Model}, journal ={Parallel, Distributed, and Network-Based Processing, Euromicro Conference on}, volume = {0}, year = {2004}, issn = {1066-6192}, pages = {103}, doi = {http://doi.ieeecomputersociety.org/10.1109/EMPDP.2004.1271434}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Parallel, Distributed, and Network-Based Processing, Euromicro Conference on TI - Parallelization of Time Series Forecasting Model SN - 1066-6192 SP EP A1 - J. M. Górriz, A1 - Carlos G. Puntonet, A1 - Moisés Salmerón, A1 - R. Martin-Clemente, PY - 2004 KW - null VL - 0 JA - Parallel, Distributed, and Network-Based Processing, Euromicro Conference on ER -

J. M. Górriz, University of Cádiz

Carlos G. Puntonet, University of Granada

Moisés Salmerón, University of Granada

R. Martin-Clemente, University of Seville

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/EMPDP.2004.1271434

In this paper we show a Parallel Neural Network (Cross-over Prediction Model) for time series statistical learning implemented in PVM ("Parallel Virtual Machine") and MPI ("Message Passing Interface"), in order to reduce computational time. Parallelization is achieved in two ways: updating autoregressive parameters via a genetic algorithm and evaluating the overall prediction function via a parallel neural Network. PVM permits an heterogeneous collection of Unix computers networked together to be viewed by our program as a simple parallel computer. We show different architectures of parallel processors systems and discuss its computing model.

Citation:

J. M. Górriz, Carlos G. Puntonet, Moisés Salmerón, R. Martin-Clemente, "Parallelization of Time Series Forecasting Model," pdp, pp.103, 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing (PDP'04), 2004

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