Forecasting Tourism Demand with Decomposed Search Cycles

Xin Li · Rob Law

105 citations0/0 files storedJournal of Travel Research

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Forecasting Techniques and Applications Diverse Aspects of Tourism Research Time Series Analysis and Forecasting

Abstract

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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References

2012Business Intelligence and Analytics: From Big Data to Big Impact 2011In Search of Attention 2012Predicting the Present with Google Trends 2007Tourism demand modelling and forecasting—A review of recent research 1988Journal of travel research 1995Forecasting tourism demand: A review of empirical research 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2011Forecasting private consumption: survey-based indicators vs. Google trends

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