{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T04:08:13Z","timestamp":1762056493444,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T00:00:00Z","timestamp":1656115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>The COVID-19 pandemic that started in 2020 has affected Thailand\u2019s 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\u2019s forecasting method appears a reasonable choice. However, the pandemic, or in a more general term, the \u201cevent\u201d, requires a subtle method to handle this extra event component. This research proposes a forecasting method based on Holt\u2019s method to better suit the time-series data affected by large-scale events. In addition, when combined with seasonality adjustment, three modified Holt\u2019s-based methods are proposed and implemented on Thailand\u2019s 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\u2019s method with seasonality and events yields the lowest MAPE of 8.64%, followed by 9.47% of Holt\u2019s method with events. Compared to the typical Holt\u2019s MAPE of 16.27%, the proposed methods are proved strongly effective for time-series data containing the event component.<\/jats:p>","DOI":"10.3390\/data7070086","type":"journal-article","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T10:39:13Z","timestamp":1656153553000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Event Forecasting for Thailand\u2019s Car Sales during the COVID-19 Pandemic"],"prefix":"10.3390","volume":"7","author":[{"given":"Chartchai","family":"Leenawong","sequence":"first","affiliation":[{"name":"School of Science, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}]},{"given":"Thanrada","family":"Chaikajonwat","sequence":"additional","affiliation":[{"name":"School of Science, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,25]]},"reference":[{"key":"ref_1","unstructured":"(2022, April 29). ASEAN Briefing. Available online: https:\/\/www.aseanbriefing.com\/news\/thailands-automotive-industry-opportunities-incentives\/."},{"key":"ref_2","unstructured":"(2022, April 29). CNN BUSINESS. Available online: https:\/\/money.cnn.com\/2017\/02\/20\/autos\/traffic-rush-hour-cities\/index.html."},{"key":"ref_3","unstructured":"(2022, April 29). Mashable. Available online: https:\/\/mashable.com\/article\/bangkok-traffic-jams."},{"key":"ref_4","unstructured":"(2022, April 29). Focus2move. Available online: https:\/\/www.focus2move.com\/thailand-best-selling-car\/#:~:text=Thailand%27s%20best%2Dselling%20car%20ranking,units%20sold%20(%2D4.8%25)."},{"key":"ref_5","unstructured":"(2022, April 28). World Health Organization. Available online: https:\/\/www.who.int\/emergencies\/disease-outbreak-news\/item\/2020-DON234."},{"key":"ref_6","unstructured":"(2022, April 28). World Health Organization. Available online: https:\/\/www.who.int\/news\/item\/13-10-2020-impact-of-covid-19-on-people\u2019s-livelihoods-their-health-and-our-food-systems."},{"key":"ref_7","unstructured":"(2022, April 28). The World Bank. Available online: https:\/\/www.worldbank.org\/en\/country\/thailand\/publication\/monitoring-the-impact-of-covid-19-in-thailand#:~:text=Income%3A,income%20groups%20experiencing%20income%20%20declines."},{"key":"ref_8","unstructured":"Leenawong, C. (2022). Logistics Intelligence and Forecasting with Excel 365, KMITL."},{"key":"ref_9","first-page":"1","article-title":"Modelling and Forecasting for Automotive Parts Demand of Foreign Markets on Thailand","volume":"4","author":"Wirotcheewan","year":"2011","journal-title":"Asian Int. J. Sci. Technol. Prod. Manuf. Eng."},{"key":"ref_10","first-page":"161","article-title":"The Effects of Special Events on Regression for Subcompact Car Sales in Thailand","volume":"78","author":"Rattanametawee","year":"2016","journal-title":"J. Teknol."},{"key":"ref_11","first-page":"32","article-title":"Double exponential smoothing and Holt-Winters methods with optimal initial values and weighting factors for forecasting lime, Thai chili and lemongrass prices in Thailand","volume":"45","author":"Booranawong","year":"2018","journal-title":"Eng. Appl. Sci. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"183","DOI":"10.35877\/454RI.asci1167","article-title":"Comparison of Holt and Brown\u2019s Double Exponential Smoothing Methods in The Forecast of Moving Price for Mutual Funds","volume":"1","author":"Muchayan","year":"2019","journal-title":"J. Appl. Sci. Eng. Technol. Educ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15","DOI":"10.33455\/ijcmr.v1i1.83","article-title":"Forecasting the Stock Price by using Holt\u2019s Method","volume":"1","author":"Sharif","year":"2019","journal-title":"Indones. J. Contemp. Manag. Res."},{"key":"ref_14","first-page":"123","article-title":"Study of Several Exponential Smoothing Methods for Forecasting Crude Palm Oil Productions in Thailand","volume":"19","author":"Suppalakpanya","year":"2019","journal-title":"Curr. Appl. Sci. Technol."},{"key":"ref_15","first-page":"2079","article-title":"Event Index Computation for Forecasting Case Study: Car Sales in Thailand","volume":"18","author":"Rattanametawee","year":"2020","journal-title":"Thai J. Math."},{"key":"ref_16","unstructured":"(2021, August 03). The Office of Industrial Economics, Ministry of Industry, Thailand. Available online: https:\/\/indexes.oie.go.th\/industrialStatistics1.aspx."},{"key":"ref_17","unstructured":"Leenawong, C. (2022). Data Analytics with Excel for Logistics & Supply Chain Management, CU Press."},{"key":"ref_18","unstructured":"(2022, February 06). NIST, Available online: https:\/\/www.itl.nist.gov\/div898\/handbook\/."},{"key":"ref_19","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts. [2nd ed.]."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/7\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:40:00Z","timestamp":1760139600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/7\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,25]]},"references-count":19,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["data7070086"],"URL":"https:\/\/doi.org\/10.3390\/data7070086","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2022,6,25]]}}}