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

Abstract

Time series prediction is a method to predict the system behavior in the future based on current given data. Neural Networks (NNs) approach is a well-known technique that is useful for time series prediction. In the literature many NN models such Multilayer Perceptron (MLP), Pi-Sigma NN (PSNN), Recurrent NN etc. are proposed for solving time series prediction. In this paper, we use Multiplicative Neuron Model (MNM) to predict time series. For training this model, we propose use newly developed evolutionary optimization algorithm called Sine Cosine algorithm (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.