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Forecasting daily attraction demand using big data from search engines and social media

Fengjun Tian (School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang, China)
Yang Yang (Department of Tourism and Hospitality Management, Temple University, Philadelphia, Pennsylvania, USA)
Zhenxing Mao (The Collins College of Hospitality Management, California State Polytechnic University Pomona, Pomona, California, USA)
Wenyue Tang (Institute of Ecological Civilization, Jiangxi University of Finance and Economics, Nanchang, China)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Article publication date: 18 May 2021

Issue publication date: 9 August 2021

1399

Abstract

Purpose

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Design/methodology/approach

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Findings

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Practical implications

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

Originality/value

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Keywords

Citation

Tian, F., Yang, Y., Mao, Z. and Tang, W. (2021), "Forecasting daily attraction demand using big data from search engines and social media", International Journal of Contemporary Hospitality Management, Vol. 33 No. 6, pp. 1950-1976. https://doi.org/10.1108/IJCHM-06-2020-0631

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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