Decomposition Method with Application of Grey Model GM(1,1) for Forecasting Seasonal Time Series.

Forecasting is one of the activities companies need to determine the policies that need to be taken for the continuity of operations. There are many methods for forecasting, one of which is the grey model GM(1,1). The GM(1,1) is one of the successful forecasting methods applied to economics, finance, engineering, and others. However, according to several previous studies, the GM(1,1) is not good enough to forecast data containing seasonal characteristics. Therefore, this study aims to develop a hybrid model so that the GM(1,1) can forecast seasonal time series. The hybrid model combines the decomposition method for seasonality adjustment and the grey model GM(1,1) for forecasting seasonal time series. The results are compared to the seasonal grey model SGM(1,1). Based on the evaluation using error criteria, it is found that the hybrid model is the best. [ABSTRACT FROM AUTHOR]