How to capture tourists’ search behavior in tourism forecasts? A two-stage feature selection approach

Abstract

Search engine data have been widely used and shown to be useful in tourism demand forecasting. However, considering of the vast amounts of search keywords, how to better capture the tourists’ attention and explore the most predictive keyword combination remain unsolved. In this study, a two-stage feature selection-based methodology is proposed to address this question. Specifically, i.e., single feature selection method comparison for selecting a relative effective way to reduce the data dimension and ensure the quality of the initial subset, genetic algorithm in the second stage for obtaining feature subset better suitable for forecasting model with stronger predictive power. Experimental results indicate that the two-stage feature selection method outperforms all the considered benchmarks.