A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting

• URL: http://arxiv.org/abs/2503.19423v1
• Date: Tue, 25 Mar 2025 08:02:09 GMT
• Title: A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting
• Authors: Tingting Diao, Xinzhang Wu, Lina Yang, Ling Xiao, Yunxuan Dong,
• Abstract summary: A novel framework produces realistic virtual samples by dynamically modeling correlations through a graph convolutional network.<n>Experiments on real-world daily and monthly tourism demand datasets indicate a reduction in average MASE by 18.37% compared to conventional Transformer-based models.
• Score: 3.868072865207522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate tourism demand forecasting is hindered by limited historical data and complex spatiotemporal dependencies among tourist origins. A novel forecasting framework integrating virtual sample generation and a novel Transformer predictor addresses constraints arising from restricted data availability. A spatiotemporal GAN produces realistic virtual samples by dynamically modeling spatial correlations through a graph convolutional network, and an enhanced Transformer captures local patterns with causal convolutions and long-term dependencies with self-attention,eliminating autoregressive decoding. A joint training strategy refines virtual sample generation based on predictor feedback to maintain robust performance under data-scarce conditions. Experimental evaluations on real-world daily and monthly tourism demand datasets indicate a reduction in average MASE by 18.37% compared to conventional Transformer-based models, demonstrating improved forecasting accuracy. The integration of adaptive spatiotemporal sample augmentation with a specialized Transformer can effectively address limited-data forecasting scenarios in tourism management.

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