The Statistical Forecasting of COVID-19 Infected People in Thailand
Keywords:
COVID-19, global constant mean model, local constant mean model, adaptive forecasting, Box – JenkinsAbstract
The research objectives are to compare the statistical forecasting of COVID-19 infected people in Thailand using four techniques: global constant mean model, local constant mean model, adaptive forecasting and Box - Jenkins; and to study the errors from the statistical forecasting using MAD, MSE and MAPE to select the appropriate forecasting. This research has taken the number of COVID-19 infected people from the Center for COVID-19 Situation Administration (CCSA) between 1st April 2022 and 31st October 2022 (7 months or 214 days) for forecasting COVID-19 infected people in Thailand. The research results were summarized as follows: the minimum number of COVID-19 infected people was 26 persons in April and the maximum was 23,418 in August. The average number of COVID-19 infected people for 7 months is 8,802 persons, and the top three with the least MAD, MSE and MAPE for statistical forecasting of COVID-19 infected people in Thailand are single exponential smoothing with smoothing constants of = 0.1 and 0.5, and single moving average with n = 2.
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