Graph-Guided Neural Network for Tourism Demand Forecasting

An accurate tourism demand forecasting model is crucial for tourism decision-makers. In recent years, several deep learning-based models have been developed to predict tourist arrivals via search intensity indices. However, few methods consider the lag effect in the long-term time range and the interaction between different search intensity indices factors. To alleviate the above limitations, we propose a graph-guided tourism demand forecasting network, which can model the lag effect of historical variables on future variables. Specifically, each variable is individually encoded via a convolutional neural network in the time dimension. Then, lag effects are modeled dynamically in a bipartite graph, and mined via graph aggregation. Finally, a fully-connected network is designed for regression prediction. Experimental results on two public datasets demonstrate the superiority of the proposed method in both one-step and multi-step prediction compared with existing methods.