Author(s)

Qingfeng Duan ¹

Affiliation(s)

¹ School of Business, Guangxi University, Nanning, Guangxi, 530000, China

Corresponding Author

Qingfeng Duan

ABSTRACT

This study investigates the integration of search engine data into tourism demand forecasting models, specifically focusing on Hong Kong, China. Utilizing the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, we aimed to enhance prediction accuracy by incorporating search data related to tourist interests and activities. Our results demonstrate that the SARIMAX model, which includes exogenous search data, significantly outperforms the traditional SARIMA model in forecasting tourism demand. The study highlights the importance of using big data sources to capture real-time shifts in tourist behavior, providing valuable insights for stakeholders in the tourism industry to make informed decisions and tailor their strategies. The findings underscore the potential of combining traditional time series models with modern data analytics to achieve more precise and actionable forecasts in the dynamic field of tourism.

KEYWORDS

Search engine data, Tourism demand forecast, Prediction accuracy

CITE THIS PAPER

Qingfeng Duan, Predicting Tourism Demand by Combining Search Engine Data. Tourism Management and Technology Economy (2024) Vol. 7: 1-8. DOI: http://dx.doi.org/10.23977/tmte.2024.070301.

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