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Subject: search.filters.subject.Box-Jenkins method

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    Forecasting Models for Total Crude Palm Oil Productions in Thailand
    (2024-12-02)
    Banditvilai, Somsri
    ;
    Araveeporn, Autcha
    This research aims to find a suitable forecasting model for Thailand's total crude palm oil production. The monthly total crude palm oil production in Thailand was gathered from the Office of Agricultural Economics, Ministry of Agriculture, and cooperatives from January 2010 to December 2022. The data were divided into two sets. The first set, from January 2010 to December 2021, was used for constructing and selecting the forecasting models. The second one, from January 2022 to December 2022, was used to compute the accuracy of the forecasting model. Since the total crude palm oil production has trend and seasonal variation, the research used the Holt-Winters method with different initial settings for trend and seasonal influence, the Bagging Holt-Winters method, and the Box-Jenkins method to construct the forecasting models. The minimum mean square error (MSE) and residuals have normal distributions used to select the appropriate forecasting model, and the mean absolute percentage error (MAPE) was used to compute the efficiency of the forecasting model.According to the three forecasting methods results, the Box-Jenkins method was suitable for forecasting Thailand's total crude palm oil production. The ARIMA(2,1,2)(0,1,1)12 model was the best model for predicting Thailand's total crude palm oil production and yielded the MAPE =13.49%
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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%
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    Inventory Management for Consumer Products: A Distribution Center in Thailand
    (2022-01-01)
    Leerojanaprapa, Kanogkan
    ;
    Bhundarak, Komn
    ;
    Atthirawong, Walailak
    ;
    Sirikasemsuk, Kittiwat
    The objective of the study is to formulate an appropriate order quantity policy. A best-selling product, a cleaner fresher mouth, was selected to be a representative of this study. Monthly Sales data of the cleaner fresher product was collected from January 2013 to December 2021, for 9 years, to forecast the sales of each product. Two forecasting techniques i.e. Exponential Smoothing and Box-Jenkins methods were compared to select the appropriate forecasting method by using MSE criterion. It was found that the Triple Exponential Method was the suitable forecasting model to forecast demand for the cleaner fresher products. These forecast values were then applied in inventory planning to determine the order policy by using Economic Order Quantity (EOQ) method of inventory management. The results found that EOQ technique resulted in a reduction in total cost rather than the company's current technique by 27.80%•
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    Forecasting the Number of Hat Yai International Airport Passengers
    (2021-06-04)
    Banditvilai, Somsri
    ;
    Kuharattanachai, Choojai
    The objective of this research is to compare three forecasting techniques of the Holt-Winters method with different initial values, the Bagging Holt-Winters method, and Box-Jenkins method based on the number of monthly Hat Yai International airport passengers from January 2003 to December 2019 which have both non-linear trend and seasonal variation. The data are collected by the Airport of Thailand Public Company Limited. The data are divided into 2 sets. The first set from January 2003 to December 2018 is used to construct the models and employed minimum Root Mean Square Error (RMSE) and residuals have normal distribution for model selection. The second set is from January 2019 to December 2019 which is used to compute the accuracy of forecasting models by using the Mean Absolute Percentage Error (MAPE). The results show that the additive Bagging Holt-Winters model gives the minimum RMSE = 9,0031.66 for the first set and yields MAPE = 11.66% for the second one.
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    Forecasting models of Chinese, Malaysian and South Korean tourists visiting Thailand
    (2019-07-08)
    Banditvilai, Somsri
    ;
    Anansatitzin, Siriluck
    This research presents a comparative study of two different forecasting methods based on the monthly Chinese, Malaysian, and South Korean tourists visiting Thailand. Holt-Winters method and Box-Jenkins method are compared. The data are taken from the Tourism Authority of Thailand, Ministry of Tourism and Sports starting from January, 2007 to December, 2018. The data are divided into 2 sets. The first set from January, 2007 to December, 2017 is used for constructing and selection the forecasting models. The second set from January, 2018 to December, 2018 is used for computing the accuracy of the forecasting model. The forecasting models are chosen by considering the smallest root mean square error (RMSE). The mean absolute percentage error (MAPE) is used to measure the accuracy of the model. The results show that Additive Holt-Winters method obtains the smallest RMSE for Chinese tourists and Box-Jenkins method gain the smallest RMSE for both Malaysian and South Korean tourists in the modeling process. While MAPE in the forecasting process for China, Malaysia and South Korea tourists are 10.22%, 11.71% and 5.14% respectively.