Year: 2025
Type: article
Abstract: Tourism is a crucial driver of national economies, contributing to leisure, cultural exchange, and economic growth through key sectors such as hospitality and transportation. Malaysia, a prominent tourist destination in Southeast Asia, welcomed millions of international visitors in 2019, significantly boosting its economy. However, the COVID-19 pandemic led to a sharp decline in tourist arrivals, highlighting the industry's vulnerability to global disruptions. This study models tourist arrivals in Malaysia using Univariate Time Series Models based on data from January 2012 to December 2019, obtained from the Tourism Malaysia Department. Five forecasting models—Naïve Model, Seasonal Naïve Model, Single Exponential Smoothing (SES), Holt’s Linear Trend Method, and Holt-Winters’ Method—were evaluated using error metrics, including Mean Absolute Scaled Error (MASE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results indicate that the Seasonal Naïve Model produced the lowest MAE and MAPE values, making it the most accurate model for forecasting tourist arrivals. These findings provide valuable insights for policymakers and tourism industry stakeholders in strategic planning and resource allocation.
Institutions: Universiti Teknologi MARA, Universiti Teknologi MARA System
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
Funder: Universiti Teknologi MARA
Award: G854476752
OpenAlex ID: W4412676025