Event Forecasting for Thailand’s Car Sales during the COVID-19 Pandemic

Chartchai Leenawong and Thanrada Chaikajonwat
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Chartchai Leenawong: School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Thanrada Chaikajonwat: School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand

Data, 2022, vol. 7, issue 7, 1-15

Abstract: The COVID-19 pandemic that started in 2020 has affected Thailand’s automotive industry, among many others. During the several stages of the pandemic period, car sales figures fluctuate, and hence are difficult to fit and forecast. Due to the trend present in the sales data, the Holt’s forecasting method appears a reasonable choice. However, the pandemic, or in a more general term, the “event”, requires a subtle method to handle this extra event component. This research proposes a forecasting method based on Holt’s method to better suit the time-series data affected by large-scale events. In addition, when combined with seasonality adjustment, three modified Holt’s-based methods are proposed and implemented on Thailand’s monthly car sales covering the pandemic period. Different flags are carefully assigned to each of the sales data to represent different stages of the pandemic. The results show that Holt’s method with seasonality and events yields the lowest MAPE of 8.64%, followed by 9.47% of Holt’s method with events. Compared to the typical Holt’s MAPE of 16.27%, the proposed methods are proved strongly effective for time-series data containing the event component.

Keywords: Thailand’s car sales; Holt’s method; event component; COVID-19; seasonality (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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