Journal
JOURNAL OF TRAVEL RESEARCH
Volume 59, Issue 1, Pages 52-68Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/0047287518824158
Keywords
Google search data; ensemble empirical mode decomposition; tourism demand; tourism forecasting
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Funding
- National Natural Science Foundation of China [71601021]
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This study aims to examine whether decomposed search engine data can be used to improve the forecasting accuracy of tourism demand. The methodology was applied to predict monthly tourist arrivals from nine countries to Hong Kong. Search engine data from Google Trends were first decomposed into different components using an ensemble empirical mode decomposition method and then the cyclical components were examined through statistical analysis. Forecasting models with rolling window estimation were implemented to predict the tourist arrivals to Hong Kong. Results indicate the proposed methodology can outperform the benchmark model in the out-of-sample forecasting evaluation of Choi and Varian (2012). The findings also demonstrate that our proposed methodology is superior in forecasting turning points. This study proposes a unique decomposition-based perspective on tourism forecasting using online search engine data.
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