Diverse feature extraction techniques in internet search query to forecast tourism demand: An in-depth comparison

Abstract

AbstractIt is an effective approach to improve forecasting by extracting effective information from large panels of search query data. Feature extraction techniques (FETs) can extract information from all features by creating new fewer features based on algebraic transformation; however, they have not been extensively investigated and compared for tourism forecasting. We employ five FETs to process multi-dimensional search query data, and build a bunch of models based on econometrics, machine learning, ensemble learning and hybrid methods. The improving performances of FETs based on tourism demand forecasting in Sanya after COVID-19 and in Macau before COVID-19 are evaluated. The results show that forecasting models with FETs outperform the benchmark model SARIMAX without FETs, which demonstrates the efficacy of FETs in search query data extraction. This study provides meaningful guidance for improving the quality of multi-dimensional data and optimizing tourism forecasting.