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Author: search.filters.author.Rattanametawee, WitchayaSubject: search.filters.subject.Car sales

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    Event index computation for forecasting case study: Car sales in Thailand
    (2020-12-01)
    Rattanametawee, Witchaya
    ;
    Leenawong, Chartchai
    Due to the impact of special events, both positive and negative, on the sales data, the ordinary Time-series Decomposition (TSD) forecasting model cannot merely capture these effects, even with the added seasonality and trends. Therefore, in this research, a new method for computing the event indices, representing the unusual fluctuations for a certain period in the time series, is proposed in order for it to be incorporated into TSD, alongside the conventional trend, seasonal, and cyclical components. A case study of subcompact car sales monthly data in Thailand during the years 2011-2018 is examined as for that time period contains the 2011 nationwide big flood reflecting the negative impact, as well as the nation’s tax-incentive first-car buyer scheme reflecting the positive impact on the dataset. The mean absolute percentage error (MAPE) is used as an accuracy measure of the proposed forecasting model and it illustrates the promising results in the end.
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    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).