@article{33a6bc728026467987005ea1bf173996,
title = "Hierarchical pattern recognition for tourism demand forecasting",
abstract = "This study proposes a hierarchical pattern recognition method for tourism demand forecasting. The hierarchy consists of three tiers: the first tier recognizes the calendar pattern of tourism demand, identifying work days and holidays and integrating “floating holidays.” The second tier recognizes the tourism demand pattern in the data stream for different calendar pattern groups. The third tier generates forecasts of future tourism demand. Evidence from daily tourist visits to three attractions in China shows that the proposed method is effective in forecasting daily tourism demand. Moreover, the treatment of “floating holidays” turns out to be more effective and flexible than the commonly adopted dummy variable approach.",
keywords = "Calendar pattern, Daily attraction visits, Floating holidays, Hierarchical pattern recognition, Tourism demand forecasting, Tourism demand pattern",
author = "Mingming Hu and Qiu, \{Richard T.R.\} and Wu, \{Doris Chenguang\} and Haiyan Song",
note = "Funding Information: Due to the exponential development in computing technology, AI-based techniques have received substantial attention in various scientific disciplines. In the tourism demand forecast context, AI-based techniques aim to establish non-linear connections between tourism demand, its lagged values, and other explanatory variables (Claveria, Monte, \& Torra, 2015; Kon \& Turner, 2005; Law, 2000; Law \& Au, 1999; Palmer, Montano, \& Ses?, 2006). Artificial neural networks (Law, 2000; Law \& Au, 1999), support vector regression models (Chen \& Wang, 2007), and Gaussian> process regression (Wu, Law, \& Xu, 2012) are typical AI-based techniques found in the tourism demand forecasting literature. AI-based techniques are sometimes referred to as a ?black box? (Zhang, Patuwo, \& Hu, 1998), due to the lack of theoretical foundation for the estimation process. Nevertheless, the demand for high-accuracy forecasting has made AI-based techniques very popular in the tourism demand forecast literature since 2000 (Song et al., 2019). The authors would like to thank the financial supports from the National Natural Science Foundation of China (No. 71761001; No. 71573289), the Hong Kong Scholars Program (To M. Hu, PolyU Project ID: P0011277), the Start-up Research Grant of University of Macau (SRG2019-00182-FBA), and Guangxi Development Strategy Institute (No.： 2020GDSIYJ05). Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",
year = "2021",
month = jun,
doi = "10.1016/j.tourman.2020.104263",
language = "English",
volume = "84",
journal = "Tourism Management",
issn = "0261-5177",
publisher = "Elsevier Ltd",
}