VMD-Enhanced Multi-Model Ensemble Framework for Tourism Demand Forecasting: A Feature Engineering Approach

This paper proposes a novel variational mode decomposition (VMD)-enhanced ensemble framework for tourism demand forecasting, which achieves competitive predictive performance. The core innovation is a structured hybrid methodology that first employs VMD to decompose complex temporal signals, from which multiple interpretable features—encoding trend, volatility, and momentum characteristics—are extracted. These enriched features are then processed by an intelligently weighted ensemble of three diverse models: support vector regression (SVR), gradient boosting regression (GBR), and random forest (RF). An adaptive weighting mechanism dynamically optimizes the contribution of each base model, enhancing robustness and accuracy. Comprehensive experiments demonstrate the framework’s superior effectiveness. Compared to strong contemporary benchmarks such as CNN-LSTM, our approach achieves significant error reduction: an RMSE of 0.837 (16.29% improvement), an MAE of 0.584 (12.19% improvement), and a MAPE of 15.42% (13.38% improvement). These results validate that the integration of VMD-based feature engineering with an adaptive multi-model ensemble effectively captures complex temporal dynamics and mitigates individual model limitations.