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Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting

Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting

Ratnadip Adhikari, R. K. Agrawal
Copyright: © 2013 |Volume: 4 |Issue: 3 |Pages: 16
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466634213|DOI: 10.4018/jaec.2013070107
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MLA

Adhikari, Ratnadip, and R. K. Agrawal. "Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting." IJAEC vol.4, no.3 2013: pp.75-90. http://doi.org/10.4018/jaec.2013070107

APA

Adhikari, R. & Agrawal, R. K. (2013). Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting. International Journal of Applied Evolutionary Computation (IJAEC), 4(3), 75-90. http://doi.org/10.4018/jaec.2013070107

Chicago

Adhikari, Ratnadip, and R. K. Agrawal. "Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting," International Journal of Applied Evolutionary Computation (IJAEC) 4, no.3: 75-90. http://doi.org/10.4018/jaec.2013070107

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Abstract

Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward as well as recurrent neural networks through training with PSO. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are used to train feedforward ANN (FANN) and Elman ANN (EANN) models. A novel nonlinear hybrid architecture is proposed to incorporate the training strengths of all these three PSO algorithms. Experiments are conducted on four real-world time series with the three forecasting models, viz. Box-Jenkins, FANN and EANN. Obtained results clearly demonstrate the superior forecasting performances of all three PSO algorithms over their BP counterpart for both FANN as well as EANN models. Both PSO and BP based neural networks also achieved notably better accuracies than the statistical Box-Jenkins methods. The forecasting performances of the neural network models are further improved through the proposed hybrid PSO framework.

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