Ensemble Recurrent Neural Network Design Using a Genetic Algorithm Applied in Times Series Prediction
Abstract: This paper shows a new method based on ensemble recurrent neural networks for time series prediction. The proposed method seeks 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 Systems. The optimization consists of the modules, hidden layer, neurons of the ensemble neural network. The fuzzy system used is of Mamdani type, which has five input variables and one output variable, and 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. In this way was shown that the method is effective for time series Prediction.
| Main Authors: | , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Instituto Politécnico Nacional, Centro de Investigación en Computación
2022
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| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462022000200683 |
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