GOLDEN EXPONENTIAL SMOOTHING: A SELF-ADJUSTED METHOD FOR IDENTIFYING OPTIMUM ALPHA

The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) is proposed to solve the problem of choosing the optimum alpha. The conventional method needs human intervention in which the forecaster would determine the most suitable alpha or else the prediction accuracy will be affected. This method is reformed and interposed with Golden Section Search such that an optimum alpha could be identified during the algorithm training process. Numerical simulations of four sets of times series data are employed to test the efficiency of the GES model. The findings show that the GES model is self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which is identified from the algorithm training stage, demonstrates good performance in the stage of Model Testing and Usage.