Forecasting international tourist arrivals in Malaysia using holt-winters model - Ismail, Fauziah; Sha’ari, Nur’ Aina; Mohd Zuhairi, Amla Ilyana; Norshahidi, Nur Dalila; Wan Husin, Wan Zakiyatussariroh (GADING Journal for the Social Sciences, 2025)
Malaysia's tourism sector has been significantly impacted by the COVID-19 pandemic, especially in terms of foreign visitor arrivals. The aim of this research is to forecast the future number of international tourist arrivals using the Holt-Winters additive method. Additionally, this study analyses the current trends of international tourist arrivals in Malaysia. The dataset employed spans from January 2012 to June 2023, encompassing periods before, during, and after the COVID-19 pandemic. The estimation of the Holt-Winters model is based on 126 observations (January 2012 until June 2022), while the validation of the model is based on 12 observations (from July 2022 to June 2023). Based on the estimated Holt-Winters model, the forecast value of monthly number of international tourist arrivals in Malaysia was generated for July 2023 to June 2024. The trend analysis shows that the number of Malaysia’s tourist arrivals fluctuated significantly from one month to another from January 2012 to January 2020. Since then, the number of tourist arrivals progressively declined to its lowest level starting in April 2020 during the implementation of the travel restrictions policy due to the COVID-19 pandemic and the trend remained constant until March 2022. The number of tourist arrivals then slowly increased in May 2022 until now. As it turned out, the forecast generated for the next 12 months is closer in reaching Malaysia Tourism's target of 27.3 million tourists by the end of 2024. The results demonstrate that, despite short-term fluctuations, the overall trend indicates a gradual increase in the number of international tourist arrivals to Malaysia between July 2023 to June 2024. This indicates that the trend of international tourist arrivals in Malaysia is returning to the normal pattern observed prior to the COVID-19 pandemic.