Bird Swarm Algorithm and Particle Swarm Optimization in Ensemble Recurrent Neural Networks Optimization for Time Series Prediction

Abstract

Recurrent Neural Network have proven to provide good results in time series prediction. In this paper, an ensemble recurrent neural network, for time series is used. Euro/Mexican pesos and Euro/Dollar series are utilized to develop a prediction model. The design of this consists of an ensemble recurrent neural network and the optimization of the structure of this network is achieved with the bird swarm algorithm and Particle Swarm Optimization. The outputs of the networks are integrated with type-1 and type-2 fuzzy systems. These fuzzy systems are of the Mamdani type. The tests were realized with the designed method and a good result was obtained in the application of time series, as well as a comparison of the two optimization algorithms was made.