Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

Abstract

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

Forecasting daily attraction demand using big data from search engines and social media

“…In fact, internet big data (i.e. search engine and social media data) have gained increasing popularity and momentum in forecasting as they can capture consumer behaviors and external factors to improve tourism forecasting performance (Wen et al , 2020). Serving as powerful predictors for tourism demand, search trend and social media data have become favorable sources, and major big data types for tourism forecasting as searches and social activities are usually conducted in private and by a much larger tourist population (Yang et al , 2014; Li et al , 2020; Li et al , 2017).…”

“…This model returned better forecasting performance than component models. Wen et al (2020) constructed a MIDAS-SARIMA integrated model to forecast Hong Kong’s monthly tourist arrivals from mainland China. The improved MIDAS model outperformed benchmark models and traditional MIDAS.…”

Ex Ante Tourism Forecasting Assessment

“…More advanced forecasting models have been developed over the past two decades. They include the time-varying parameter model by Song and Wong (2003) and Page, Song, and Wu (2012), the linear almost ideal demand system (LAIDS) model by Li, Song, and Witt (2004) and De Mello and Fortuna (2005), the spatial panel models by Yang and Zhang (2019) and Long, Liu, and Song (2019), forecasting combination by Li, Song, and Witt (2006) and Li et al (2019), judgmental forecasting by Lin, Goodwin, and Song (2014) and Song, Gao, and Lin (2013), and mixed frequency data models by Hirashima et al (2017) and Wen et al (2020). The newly developed methods have shown their superiority in forecasting practice.…”

Tourism Growth Prediction Based on Deep Learning Approach

“…Web technology advancements have made search engines an essential tool for tourists when planning their trips, especially in getting relevant information on their areas of interest. Search Intensity Indices (SIIs) have been recognized as a potential TD indictor in the destination market [9,10] and have been examined by many researchers for TD prediction [11]. SII data are important for accurate TD prediction even though some practitioners have reported some challenges in using them with the conventional prediction models.…”