DocumentCode :
2708161
Title :
PSO based neural network for time series forecasting
Author :
Jha, Girish K. ; Thulasiraman, Parimala ; Thulasiram, Ruppa K.
Author_Institution :
Dept. of Comput. Sci., Univ. of Manitoba, Winnipeg, MB, Canada
fYear :
2009
fDate :
14-19 June 2009
Firstpage :
1422
Lastpage :
1427
Abstract :
Artificial neural networks are being widely used for time series forecasting. In recent years much effort has been made for the development of particle swarm algorithm for the optimization of neural networks. In this paper, the performance of two variants of particle swarm optimization algorithm (Trelea I and Trelea II) for training neural network has been examined with a real data for financial time series forecasting. Results clearly indicated the superiority of swarm based algorithms over the standard backpropagation training algorithm with respect to common performance measures across three forecasting horizons. In particular, with the Trelea II trained model, we obtained 92.48 %, 56.64 %, and 44.66 % decrease in terms of MSE over the standard back-propagation trained neural network for 10 days, 30 days and 60 days ahead forecasts respectively.
Keywords :
backpropagation; forecasting theory; neural nets; particle swarm optimisation; time series; Trelea I; Trelea II; artificial neural network; financial time series forecasting; neural network optimization; particle swarm optimization algorithm; standard backpropagation training algorithm; Artificial neural networks; Backpropagation algorithms; Computational intelligence; Computer architecture; Computer networks; Genetic algorithms; Neural networks; Particle swarm optimization; Predictive models; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
ISSN :
1098-7576
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2009.5178707
Filename :
5178707
Link To Document :
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