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Author: search.filters.author.Banditvilai, SomsriSubject: search.filters.subject.Forecasting

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    Forecasting Models for Thailand's Electrical Appliances Export Values
    (2023-01-01)
    Banditvilai, Somsri
    ;
    Klomwises, Yuwadee
    This research aimed to study forecasting models for Thailand's electrical appliances export values. Thailand's monthly electrical appliances export values were gathered from the Information Technology and Communication Center, Ministry of Commerce, from January 2006 to November 2022. The data from January 2006 to December 2021 were used to construct and select the forecasting models, and the remaining were used for measuring the model's accuracy. Since the electrical appliances export values showed trends and seasonal variation, the researcher selected the Holt-Winters method with various initial settings, the Box-Jenkins method, and Long Short-Term Memory Neural Networks (LSTM) for constructing models. The forecasting models were chosen by minimum Root Mean Square Error (RMSE) as a criterion. Mean Absolute Percentage Error (MAPE) was employed to measure the accuracy of the forecasting model. The study revealed that the Box-Jenkins model gave the appropriate forecasting model for Thailand's electrical appliances export values and gained a MAPE of 8.0%.
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    FORECASTING MODELS FOR FIRST YEAR PREMIUM OF LIFE INSURANCE
    (2022-01-01)
    Banditvilai, Somsri
    ;
    Kuharattanachai, Choojai
    The objective of this research is to study forecasting models for the first year premium of life insurance. The premium data are gathered from the Office of Insurance Commission (OIC) during January 2003 to November, 2021. The data are divided into 2 sets. The first set from January, 2003 to December 2020 is used for constructing and selection the forecasting models. The second one from January 2021 to November 2021 is used for computing the accuracy of the forecasting model. The forecasting models are chosen by considering the minimum Root Mean Square Error (RMSE). The Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the model. The results showed that the multiplicative model with initial values from 18 years Decomposition method give the appropriate model for the first year premium of life insurance and yields the MAPE = 17.29%