Comparison of Time Series Models for Forecasting Pneumonia Cases in Thailand
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Abstract
The objective of this research was to compare forecasting techniques to find an appropriate model for forecasting numbers of pneumonia cases in Thailand, which consists of obvious trend and seasonality in time series. Three forecasting methods were investigated including the classical decomposition method, Winter’s multiplicative method and Box-Jenkins method. The numbers of pneumonia cases data reported quarterly from 2008 to 2018 were used. Compared the suitable forecasting model under the smallest mean absolute percentage error (MAPE) criterion. The results showed that the Box-Jenkins method gave the lowest MAPE. The appropriate model for forecasting the number of pneumonia cases in Thailand was the autoregressive integrated moving average model .
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References
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