Comparison of GM(1,1), DGM(1,1), FGM(1,1), and FDGM(1,1) Models for Forecasting Breast Cancer Cases
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Abstract
The objective of this study is to compare the forecasting performance of four time series models for predicting the number of breast cancer patients. The models considered include the Grey Model GM(1,1), the Discrete Grey Model DGM(1,1), the Fourier Grey Model FGM(1,1), and the Fourier Discrete Grey Model FDGM(1,1). The FGM(1,1) and FDGM(1,1) models were enhanced using Fourier series to improve forecasting accuracy. Annual data from the National Cancer Institute were used for the analysis. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the FDGM(1,1) model achieved the highest accuracy, with a MAPE value of 15.25%, whereas the GM(1,1), DGM(1,1), and FGM(1,1) models yielded MAPE values of 17.78%, 17.41%, and 15.49%, respectively. According to the National Statistical Office’s criteria, a MAPE between 10% and 20% indicates a reasonably accurate forecasting model. Therefore, it can be concluded that, for this study, the FDGM(1,1) model is the most appropriate choice for forecasting the number of breast cancer patients.
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ลิขสิทธ์ ของมหาวิทยาลัยเทคโนโลยีราชมงคลพระนครReferences
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