LAUSR

Abstract Precise tourist demand forecasting is crucial owing to its relevance in tourism decision-making. This study proposes a novel model for tourist demand forecasting on the basis of denoising and potential factors. The denoising strategy is proposed to improve the tourist demand forecasting, and an effective and promising denoising method based on Hilbert–Huang transform is developed. Two case studies are conducted to verify the validity and predictability of the proposed model. Results indicate that denoising remarkably improves the forecasting accuracy, and the proposed denoising technique outperforms other approaches. Furthermore, the proposed model exhibits the most satisfactory forecasting performance among all benchmark models, as well as excellent scalability and stability.