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

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.

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