Forecasting tourist arrivals at attractions: Search engine empowered methodologies

Abstract

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro-level by predicting the number of visits to London museums. The number of visits to London museums is forecasted and the predictive powers of Naïve I, seasonal Naïve, SARMA, SARMAX, SARMAX-MIDAS and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models' forecasting accuracy varies for short-and long-term demand predictions. The application of higher-frequency search query data allows generation of weekly predictions, which are essential for attraction-and destination-level planning.

The role of disaggregated search data in improving tourism forecasts: Evidence from Sri Lanka

“…Literature points out that extensions of univariate models to include explanatory may improve forecasts. However, there is limited availability of data types that can be used as explanatory variables in forecasting models which makes implementing such models challenging (Volchek et al, 2019). Use of online big data has emerged as a solution to this in the literature (Jiao & Chen, 2019).…”

“…In 2019, Sri Lanka was a highly ranked tourism destination by Lonely Planet ('Best in travel in 2019') and CNN Travel ('20 best places to visit in 2020'). Accurate forecasting is often constrained due to the lack of data (Volchek et al, 2019). Sri Lanka's tourism agencies maintain records on monthly tourist arrivals and guest night data by individual source countries and regions in their administrative reports.…”

An Exploratory Analysis of Geotagged Photos From Instagram for Residents of and Visitors to Vienna

“…The main theoretical contribution of this study consists of taking the consequences of asymmetric information on current and predicted future behavior of residents and visitors into account as its conceptual framework, while applying an appropriate, structured big data analytics method for social media similar to the one suggested by Miah et al (2017). The main methodological contribution of this research pertains to the application of the mixed data sampling (MIDAS) model class when assessing the data’s temporal dimension (see Temporal Dimension of the Data: Usability of Instagram Data to Forecast Actual Tourist Arrivals section), which still has not been widely used in tourism demand modeling and forecasting to date (see, e.g., Bangwayo-Skeete & Skeete, 2015; Gunter et al, 2019; Volchek et al, 2019, for some notable examples), thereby also going beyond the simple univariate time-series techniques proposed by Miah et al (2017).…”

“…As the frequency of the forecast variable (monthly total tourist arrivals) differs from the frequency of the potential predictors (daily number of likes, comments, and photos), the MIDAS model class (Andreou et al, 2010; Ghysels et al, 2005, 2006) becomes a viable option. It has been successfully applied in various fields, including tourism demand modeling and forecasting (e.g., Bangwayo-Skeete & Skeete, 2015; Gunter et al, 2019; Volchek et al, 2019). A generic MIDAS model for the low-frequency forecast variable x reads:…”

Terrorism and Wine Tourism: The Case of Museum Attendance

“…Recent research suggests that using Internet search indexes relating to a destination improves the accuracy of time series models in forecasting the tourism demand (Önder and Gunter, 2016). Addressing individual attractions, Volchek et al (2018) find a high correlation between Google Search queries concerning the most popular London museums and actual visits. Baidu Analytics is used by Huang, Zhang, and Ding (2017) to forecast flows of visitors at the Forbidden Palace in Beijing.…”