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The purpose of this research was to construct the most suitable forecasting model for the number of patients with disease surveillance in Thailand. The data gathered from the website of Social and Quality of Life Database System during the first quarter, 2003 to the fourth quarter, 2017 (60 values) were used and divided into two categories. The first category had 56 values, which were the data during the first quarter, 2003 to the fourth quarter, 2016 for the modeling by the methods of Box-Jenkins, Winters’ exponential smoothing, and decomposition. The second category had 4 values, which were the data during the first quarter to the fourth quarter, 2017 for checking the accuracy of the forecasting models via the criterion of the lowest mean absolute percentage error. The results showed that among all forecasting methods that had been studied, Winters’ multiplicative exponential smoothing method was the most suitable for this time series.
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