Ensemble Recurrent Neural Network using Genetic Algorithm applied in Times Series Prediction

Martha Pulido, Patricia Melin


This paper shows a new method of ensemble recurrent neural networks for time series prediction. The proposed method we seek to find the structure of ensemble recurrent neural network and its optimization with Genetic Algorithms applied to the Prediction of time series. For this method, two systems are proposed to integrate responses ensemble recurrent neural network that are type-1 and Interval type-2 Fuzzy System. The optimization consists of the modules, hidden layer, neurons of the ensemble neural network. The fuzzy system used is of type Mamdani, which has five inputs variables and one output variable, the number of inputs of the fuzzy system is according to the outputs of Ensemble Recurrent Neural network. Test are performed with Mackey Glass benchmark, Mexican Stock Exchange, Dow Jones and Exchange Rate of US Dollar/Mexican Pesos. tis in this was show that the method is effective for time series Prediction.


Time series prediction, genetic algorithm, ensemble recurrent neural network

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