A New Framework to Forecast Tourism Demand with AI and Search Data
A novel two-layer multivariate decomposition ensemble framework leverages machine learning for superior spatiotemporal tourism demand forecasting. This advanced approach integrates internet search indices with tourist arrival data to model complex, multidimensional factors influencing travel trends. The methodology employs multivariate empirical mode decomposition and variational mode decomposition for sophisticated feature extraction, reducing data complexity. The extracted features are then predicted using a multivariate gated recurrent unit, a type of neural network, with final forecasts achieved through ensemble aggregation. Empirical validation on case studies in Nanjing and Haikou demonstrates this framework’s enhanced predictive accuracy over traditional baseline models, effectively uncovering latent patterns in tourism dynamics.
Study Significance: For professionals in data science and machine learning, this research showcases a practical application of advanced ensemble methods and neural networks like GRUs to solve complex, real-world forecasting problems. It highlights the strategic value of integrating heterogeneous data sources and employing multi-step decomposition for feature engineering, directly relevant to improving model performance in time-series analysis. This work provides a blueprint for applying similar deep learning and decomposition techniques to other domains requiring robust spatiotemporal prediction, from urban planning to resource management.
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