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Parallelization of Time Series Forecasting Model
A Coruna, Spain February 11-February 13
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/EMPDP.2004.127143412th Euromicro Conference on Parallel ...
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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
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@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
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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
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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