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

Abstract

Forecasting is one of the activities needed by companies 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 study, the GM(1,1) is not good enough to forecast data containing seasonal characteristics. Therefore, the aim of this study is to develop hybrid model so that the GM(1,1) is able to forecast seasonal time series. The hybrid model is constructed by combining decomposition method for seasonality adjustment and grey model GM(1,1) for forecasting seasonal time series. The results are compared to seasonal grey model SGM(1,1). Based on the evaluation using error criteria, it is found that the hybrid model is the best model.

Access to Document

Other files and links

• Link to publication in Scopus
• Open Access Version Available

Fingerprint