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 as 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 to 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 well-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 are made with some of the results given in the literature.

Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting

“…, 𝑃𝑃𝑌𝑌 (9) 𝑔𝑔𝑛𝑛 ] (10)Step 4. Updating the inertia weight (w), cognitive coefficient (𝑐𝑐 1 ) and social coefficient (𝑐𝑐 2 ) To increase the convergence speed of PSO, the coefficients w, 𝑐𝑐 1 and 𝑐𝑐 2 are updated in the iterations as in Equation(11)(12)(13) 𝑤𝑤 = (𝑤𝑤 𝑚𝑚𝑚𝑚𝑚𝑚 − 𝑤𝑤 𝑚𝑚𝑖𝑖𝑛𝑛 ) × 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚−𝑚𝑚 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 + 𝑤𝑤 𝑚𝑚𝑖𝑖𝑛𝑛…”