This study reviews 211 key papers published between 1968 and 2018, for a better
understanding of how the methods of tourism demand forecasting have evolved over time …

Purpose The purpose of this paper is to review recent studies published from 2007 to 2015
on tourism and hotel demand modeling and forecasting with a view to identifying the …

Traditional tourism demand forecasting models may face challenges when massive
amounts of search intensity indices are adopted as tourism demand indicators. Using a …

Researchers have adopted online data such as search engine query volumes to forecast
tourism demand for a destination, including tourist numbers and hotel occupancy. However …

The tourism sector, with its perishable nature of products, requires precise estimation of
demand. To this effect, we propose a deep learning methodology, namely Bayesian …

This study reviewed 72 studies in tourism demand forecasting during the period from 2008
to 2017. Forecasting models are reviewed in three categories: econometric, time series and …

Prior studies have shown that Internet search query data have great potential to improve
tourism forecasting. As such, selecting the most relevant information from large amounts of …

Purpose This study aims to provide a comprehensive review of hotel demand forecasting to
identify its key fundamentals and evolution and future research directions and trends to …

This study conducts spatial-temporal forecasting to predict inbound tourism demand in 29
Chinese provincial regions. Eight models are estimated among a-spatial models …

Suppliers of tourist services continuously generate big data on ask prices. We suggest using
this information, in the form of a price index, to forecast the occupation rates for virtually any …