Forecasting tourist arrivals with machine learning and internet search index.

Abstract Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis. Highlights • We propose kernel extreme learning machine with different kernel functions to accurately forecast tourist arrivals. • Co-integration and granger causality relationship between search engine query data and tourist arrivals are verified. • Baidu index and Google trends data are simultaneously regarded as exogenous variables to forecast Beijing tourist arrivals. • Our proposed approach significantly improves the forecasting performance better than other benchmarks used in this study. [ABSTRACT FROM AUTHOR]