Araveeporn, Autcha

Loading...
Thumbnail Image
Preferred name
Araveeporn, Autcha
Main Affiliation
Email
autcha.ar@kmitl.ac.th

Settings

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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%
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Classification Study in High-Dimensional Data of Linear Discriminant Analysis and Regularized Discriminant Analysis
    (2023-01-01)
    Araveeporn, Autcha
    ;
    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.