Empirical research has shown that incorporating big data into tourism and hospitality
forecasting significantly improves prediction accuracy. This study presents a comprehensive …

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 …

Given the importance of web search volume for reflecting tourists' preferences for certain
tourism services and destinations, incorporating these data into forecasting models can …

This study forecasts both Halal tourism demand (HTD) and the financial performance of
Halal tourism industry of Malaysia using machine learning. Based on the data over the …

Big data technology has revolutionized the research paradigm of economic forecasting
regardless of the data source, forecasting method, or forecasting result. This study evaluates …

It is an effective approach to improve forecasting by extracting effective information from
large panels of search query data. Feature extraction techniques (FETs) can extract …

Purpose While relevant research has considered aggregated data from mobile devices and
personal computers (PCs), tourists' search patterns on mobile devices and PCs differ …

This study explores the possibility of using a machine learning approach to analysing social
media big data for tourism demand forecasting. We demonstrate how to extract the main …

This study aims to establish the nonlinear relationship between high frequency Baidu search
volumes, low frequency tourist arrivals and dummy variables and proposes a mixed …

Accurate forecasting of duty-free shopping demand plays a pivotal role in strategic and
operational decision-making processes. Despite the extensive literature on sustainability …