A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting

Abstract

Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method. Dealing with confusion that often researchers find in selecting an alpha (α) value among exponential smoothing families, which suits characteristics of the investigated case. As selection of the constant value precisely contributes to reduce the forecasting deviation. This paper used the alpha (α) value in the range 0,1 to 0,9 and utilized the mean absolute percentage error (MAPE) and Mean Absolute Error (MAE) as the parameter to know the grade of prediction. In data training, the authors used Single Exponential Smoothing (SES) and Brown’s Double Exponential Smoothing (B-DES) as methods to compare the results of prediction. It is addressed that forecasting with alpha (α) 0,1 is the most optimal values for Single Exponential Smoothing (SES) in this case with margin error 0,00036 of MAPE and 16,84 of MAE.