A NOVEL MULTIPLICATIVE NEURON MODEL BASED ON SINE COSINE ALGORITHM FOR TIME SERIES PREDICTION

Authors : Erdinç KOLAY

Pages : 153-160

Doi:10.18038/aubtda.443510

View : 16 | Download : 7

Publication Date : 2019-06-01

Article Type : Research Paper

Abstract :Time series prediction is a method to predict the system behavior in the future based on current given data. Neural Networks insert ignore into journalissuearticles values(NNs); approach is a well-known technique that is useful for time series prediction. In the literature many NN models such Multilayer Perceptron insert ignore into journalissuearticles values(MLP);, Pi-Sigma NN insert ignore into journalissuearticles values(PSNN);, Recurrent NN etc. are proposed for solving time series prediction. In this paper, we use Multiplicative Neuron Model insert ignore into journalissuearticles values(MNM); to predict time series. For training this model, we propose use newly developed evolutionary optimization algorithm called Sine Cosine algorithm insert ignore into journalissuearticles values(SCA);, and this algorithm has not been used as far as we know in training the MNM. The proposed SCA-MNM model is employed for the most known time series problems. In this paper, the application of the SCA-MNM on time prediction is illustrated using two mostly used datasets Mackey-Glass time series dataset, Box-Jenkins gas furnace dataset. To investigate the effect of the proposed SCA-MNM model, comparisons were made with some of the results given in the literature.
Keywords : Neural Networks, Multiplicative Neuron Model, Sine Cosine Algorithm, Time Series Prediction