Leenawong, Chartchai

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Leenawong, Chartchai
Alternative Name
Leenawong, Chatchai
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Email
chartchai.le@kmitl.ac.th

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    Item type:Publication,
    The effects of special events on regression for subcompact car sales in Thailand
    (2016-11-01)
    Rattanametawee, Witchaya
    ;
    Leenawong, Chartchai
    ;
    Netisopakul, Ponrudee
    This research proposes a method to dealing with multiple linear regression that integrates the seasonality as well as the effects of some special or unanticipated events for sales figures. The method is then applied to the car sales figures in Thailand after having been through the 2011 national big flood and the 2011-2012 government’s initiative tax-incentive program for boosting the automobile industry. Besides Thailand’s Gross Domestic Products (GDP) and the 12-month Loan’s Interest Rate as explanatory variables, seasonal dummy variables along with the proposed special event variables and appropriate event tagging are incorporated. The statistical results obtained from the proposed regression model with seasons and events, compared to the models with neither seasons nor both yields highest adjusted coefficient of determination (R-squre) and accuracy (MAPE).
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    Multiple linear regression using gradient descent: A case study on Thailand car sales
    (2017-01-01)
    Netisopakul, Ponrudee
    ;
    Leenawong, Chartchai
    Severe fluctuations in Thailand car sales had enormous impacts on the automobile and related industries. A reliable forecasting model is needed to accurately forecast the car sales for the next production batch. Using ten-year car sales data, this research proposes a machine learning approach using gradient descent (GD) to fitting multiple linear regression for Thailand car sales forecasts. The resulted forecasting accuracy is then compared with that of a normal equation method (NE) as well as that obtained from a statistical package (SP). First, two independent variables (2IVs): Thailand’s Gross Domestic Product and the 12-month Loan Rate are used in the proposed models. Then, dummy seasonal variables (Season) are added to the regression equations. Finally, dummy event flag variables (Event) are added. Totally, five sets of experiments are conducted. The experiment results show that NE produces the same regression equations as SP. Both GD and NE methods yield exactly the same results for 2IVs, but GD yields slightly less prediction accuracy than NE’s in Season and Event experiments. This research concludes that gradient descent has comparable forecasting accuracy to those from other methods. Nevertheless, when the regression contains dummy variables, caution is recommended.