Forecasting duty-free shopping demand with multisource data: a deep learning approach

DOI10.1007/S10479-024-05830-YMaRDI QIDQ6589110

Eric W. T. Ngai, Dong Zhang, Chong Wu, Pengkun Wu

Publication date: 19 August 2024

Published in: Annals of Operations Research (Search for Journal in Brave)

zbMATH Keywords

time seriescross-correlationdemand forecastingdeep learningmultisource dataduty-free shopping

Mathematics Subject Classification ID

Applications of statistics (62Pxx) Actuarial science and mathematical finance (91Gxx) Inference from stochastic processes (62Mxx)

Cites Work

• Composite leading search index: a preprocessing method of internet search data for stock trends prediction
• A forecasting model for time series based on improvements from fuzzy clustering problem
• Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine
• Forecasting tourism demand using fractional grey prediction models with Fourier series
• Sparse regression modeling for short- and long-term natural gas demand prediction
• Forecasting gold price with the XGBoost algorithm and SHAP interaction values

This page was built for publication: Forecasting duty-free shopping demand with multisource data: a deep learning approach

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6589110)