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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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Empirical Comparison of Forecasting Methods for Air Travel and Export Data in Thailand
(2024-12-01)
Banditvilai, Somsri
;
Araveeporn, Autcha
Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effective model, with the Mean Absolute Percentage Error (MAPE) used to gauge the accuracy. Findings indicate that the Bagging Holt–Winters method consistently outperformed the other methods across all the datasets. It effectively handles linear and non-linear trends and clear and ambiguous seasonal patterns. Moreover, the seventh initial configurationdelivered the most accurate forecasts for the Holt–Winters method and is recommended as the optimal starting point.
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A Classification Study in High-Dimensional Data of Linear Discriminant Analysis and Regularized Discriminant Analysis
(2023-01-01)
Araveeporn, Autcha
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Banditvilai, Somsri
The objective of this work is to compare linear discriminant analysis (LDA) and regularized discriminant analysis (RDA) for classification in high-dimensional data. This dataset consists of the response variable as a binary or dichotomous variable and the explanatory as a continuous variable. The LDA and RDA methods are well-known in statistical and probabilistic learning classification. The LDA has created the decision boundary as a linear function where the covariance of two classes is equal. Then the RDA is extended from the LDA to resolve the estimated covariance when the number of observations exceeds the explanatory variables, or called high-dimensional data. The explanatory dataset is generated from the normal distribution, contaminated normal distribution, and uniform distribution. The binary of the response variables is computed from the logit function depending on the explanatory variable. The highest average accuracy percentage evaluates to propose the performance of the classification methods in several situations. Through simulation results, the LDA was successful when using large sample sizes, but the RDA performed when using the most sample sizes.
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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%
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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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Comparing the first and the second orders of random coefficient autoregressive model on time series data
(2019-07-08)
Araveeporn, Autcha
;
Banditvilai, Somsri
The random coefficient autoregressive (RCA) model develops from the autoregressive model and the hierarchical model. The RCA model has considered a constant parameter and coefficient parameter depended on past data. The least squares method is a widely used method by minimizing the sum of squared residuals and differential with respect to the unknown parameter. In this paper, the concept of the least squares method is used to estimate an unknown parameter of the first and the second orders of Random Coefficient Autoregressive (RCA) model or called RCA(1) and RCA(2) models. The efficiency of the two models is to compare by considering the minimum value of mean square error. The RCA(1) and RCA(2) are then applied to a time series data in the form of nonstationary data. The monthly averages of the Stock Exchange of Thailand (SET) index and the daily volume of exchange rate Baht/Dollar are fitted on these models. The prediction of RCA(1) and RCA(2) models is shown that the RCA(l) model outperforms the RCA(2) model, similar to two data sets.
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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.
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Simulation of the night shift solid waste collection system of phuket municipality
(2017-01-01)
Banditvilai, Somsri
;
Niraso, Mantira
This research was conducted in order to simulate the night shift solid waste collection system of Phuket Municipality, Thailand. The Phuket Municipality faced the problems of residualwaste and an unbalanced load for solidwaste collection teams. The waste management committee of Phuket Municipality wanted to improve the solid waste collection system to run more efficiently. This research analyzed the volume of solid waste collection instead of the weight, and has separated the solid waste collection points into 11 “types”. The data was collected from the survey form. Minitab 16.1 was used to analyze and test the data distribution, and then used them to build themodel.MicrosoftVisual C++was used to build the simulationmodel,which was then verified and validated extensively. The model represented the actual night shift solid waste collection system of Phuket Municipality. The heuristic approach was then employed to apply new assigned zones and routings. The results from the study of the new system of night shift solid waste collection system of Phuket Municipality shows that there is no residual waste and no unbalanced load between solid waste collection teams. The new system works effectively and can decrease the total number of trips for solid waste collection by 9.1% and the average distance and time for the solid waste collection system are decreased by 7.42% and 7.10% respectively.
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Rule extraction for support vector machine using input space expansion
(2011-12-01)
Pitiranggon, Prasan
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Benjathepanun, Nunthika
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Banditvilai, Somsri
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Boonjing, Veera
Fuzzy Rule-Based System (FRB) in the form of human comprehensible IF-THEN rules can be extracted from Support Vector Machine (SVM) which is regarded as a black-boxed system. We first prove that SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS) are equivalent indicating that SVM's decision can actually be represented by fuzzy IFTHEN rules. We then propose a rule extraction method based on kernel function firing strength and unbounded support vector space expansion. An advantage of our method is the guarantee that the number of final fuzzy IF-THEN rules is equal or less than the number of support vectors in SVM, and it may reveal human comprehensible patterns. We compare our method against SVM using popular benchmark data sets, and the results are comparable. © 2011 Springer-Verlag Berlin Heidelberg.

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