Forecasting Tourism Demand in the Lower Northern Provinces of Thailand
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Abstract
Tourism demand forecasting is critical for strategic planning and resource allocation, especially in emerging tourist destinations that often lack the necessary knowledge for effective resource management planning. The objective of this study is to determine the best appropriate predictive models for effectively projecting tourism demand in the Lower Northern Provinces of Thailand, including Phitsanulok, Uttaradit, Sukhothai, Tak, and Phetchabun. These provinces are emerging tourist destinations with underdeveloped tourism infrastructure compared to major cities. The study employs four different forecasting models: Holt-Winter's Exponential Smoothing, the Box-Jenkins approach (ARIMA), Artificial Neural Networks (ANN), and the Trigonometric Box-Cox ARMA Trend Seasonal Model (TBATS). The performance of these models is evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), based on monthly tourist arrival data from January 2013 to December 2018. The findings indicate that the ARIMA model provides the highest accuracy for Phitsanulok, Uttaradit, Sukhothai, and Tak, while the ANN model performs best for Phetchabun. This study highlights the importance of selecting forecasting methods that align with the specific data pattern.
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