Tourism Demand Prediction With Tourist‐Generated Images and Text: An Ensemble Sparse ELM Approach

Abstract

This study examines the predictive value of tourist‐generated images and text for daily tourism demand forecasting. Building on established L 0 ‐regularized extreme learning machine (ELM) formulations and ADMM‐based sparse optimization, we construct an ensemble forecasting framework, termed ES‐ L 0 ‐ELM, that combines image‐derived and textual predictors with numerical variables. The framework integrates sparse output‐weight estimation with model selection and weighted aggregation. Experiments on Jiuzhaigou and Mount Siguniang show that ES‐ L 0 ‐ELM achieves lower forecasting errors than the evaluated benchmarks. On the Jiuzhaigou dataset, it reduces MAPE by 18.66% and 14.33% relative to recent tourism forecasting models BERT‐ELM and TFIDF‐Tran‐ELM, respectively. Image features provide greater predictive gains than text features, whereas their combination yields the best performance. This study provides a sparse ensemble forecasting framework and empirical evidence of the complementary value of tourist‐generated images and text.