Decomposition Methods for Tourism Demand Forecasting: A Comparative Study

Abstract

Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data’s intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases.

Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach

“…In addition to the above-mentioned basic decomposition methods, more and more advanced decomposition models introduced to this field aimed to improve the forecasting performance (Shao et al, 2021;Zhang, Jiang, et al, 2021;Zhang, Li, et al, 2021). Zhang et al (2020)…”

“…In addition to the above‐mentioned basic decomposition methods, more and more advanced decomposition models introduced to this field aimed to improve the forecasting performance (Shao et al, 2021; Zhang, Jiang, et al, 2021; Zhang, Li, et al, 2021). Zhang et al (2020) introduced seasonal and trend decomposition using Loess (STL) combined AI‐based methods to forecast Hong Kong tourist arrivals.…”

A novel deep learning approach for tourism volume forecasting with tourist search data

“…For example, Law et al (2019) designed available keywords covering various aspects of the destination and eventually collected more than 200 keywords to obtain search engine data to forecast Macau's tourism demand. Zhang, Li, Law, et al (2021) and Zhang, Li, Sun, et al (2021) extracted information from a large SED panel and combined travel‐related data of different frequencies to improve the monthly forecasting accuracy of hotel occupancy rates using frequency‐mixing techniques. Li et al (2020) combined SED with online review data to forecast the number of visitors to a scenic spot in China, verifying the validity of this data of SED in tourism demand forecasting.…”

“…Since search engine data are massive, and multidimensional big data will bring redundant information, leading to co‐linearity between variables and computational inefficiency. Therefore, dimension reduction as a necessary consideration for extracting practical information is a critical stage and has attracted the attention of many scholars (Li & Law, 2020; Sun et al, 2019; Zhang, Li, Law, et al, 2021; Zhang, Li, Sun, et al, 2021). Traditional feature transformation methods generally adopt linear dimensionality reduction methods such as factor analysis, principal component analysis (PCA), etc.…”

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

“…Thus, accurate and effective tourism demand forecasting has great practical application value ( Hu et al, 2021 ; Álvarez-Díaz & Rosselló-Nadal, 2010 ). However, tourism demand time series has distinct nonstationary, stochastic, and nonlinear characteristics that are attributed to the impact of various external factors, such as irregular events and seasonal variations ( Duan et al, 2022 ; Zhang et al, 2022 ). For example, COVID-19 caused structural breaks in global tourism markets, creating significant challenges for tourism demand forecasting performance.…”