Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Swarm intelligence
Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Neural Computation
Avoiding Pitfalls in Neural Network Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Particle swarm optimization algorithm for option pricing: extended abstract
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
International Journal of Applied Evolutionary Computation
Prediction of faults-slip-through in large software projects: an empirical evaluation
Software Quality Control
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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.