پیش بینی پروازهای ورودی گردشگران با یادگیری ماشین
Highlights
Abstract
Keywords
1. Introduction
2. Literature review
3. Kernel extreme learning machine
4. Forecasting framework
5. Experimental study
6. Conclusions
Conflicts of interest
Authors' contributions list
Acknowledgement
References
Vitae
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.
1. Introduction
All over the world, the tourism industry contributes significantly to economic growth (Gunter & Onder, 2015; Song, Li, Witt, & Athanasopoulos, 2011). According to the China National Tourism Administration, in 2016 the tourism income of China reached 4.69 trillion RMB, increasing by 13.6% compared to the previous year, and accounted for 6.3% of China's GDP. Thus, forecasting tourist volume is becoming increasingly important for predicting future economic development. Tourism demand forecasting may provide basic information for subsequent planning and policy making (Chu, 2008; Witt & Song, 2002). Methods used in tourism modeling and forecasting fall into four groups: time series models, econometrics models, artificial intelligence techniques and qualitative methods (Goh & Law, 2011; Song & Li, 2008). In addition to simple tourist data announced by the State Statistics Bureau, Internet search queries, which reflect the behavior and intentions of tourists, have increasingly been used in tourism forecasting models (Croce, 2017; Goodwin, 2008). However, the search index has created big opportunities in the modeling process of tourism forecasting (Li, Pan, Raw & Huang, 2017).