An Agent-based Method for Predicting Monthly Maximum & Minimum Quote Prices

In this paper a multi agent model for predicting monthly maximum and minimum quote prices has been proposed. This model is based on the training of Elman neural networks and using particle swarm optimization for obtaining the best parameters of the neural networks. Also one method for reducing the effects of overfitting problem is suggested. This method averages the outputs of an ensemble network, given noisy data as inout, to predict the final results.. Finally the results of using this model on a sample data set are presented and the effectiveness of this model is illustrated.

Keywords:

stock prediction , recurrent neural network , Particle Swarm Optimization , ensemble averaging