A Hybrid Forecasting Method Based on The Exponential Smoothing and Multiplicative Neuron Model Artificial Neural Network

Holt exponential smoothing method is an effective method for forecasting of non-seasonal time series. In Holt method, moving average operator with exponential decay weights is used. Multiplicative neuron model artificial neural network is a popular artificial neural network type and it has been also successfully used for the aim of forecasting of non-seasonal time series. In this study, a hybrid forecasting method that combines the properties of both Holt exponential smoothing method and multiplicative neuron model artificial neural network is proposed. The parameters and combination weights for Holt method and multiplicative neuron model are determined by particle swarm optimization. The final forecasts and confidence intervals for forecasts are obtained by using random subsampling bootstrap method. Moreover, hypothesis tests for combination weights are applied by using bootstrap samples. The proposed method is applied to Dow-Jones Industrial average stock exchange data sets between the years 2010 and 2012 and the forecasting performance of proposed method is compared with other some other methods in the literature.