用虚拟数据+增强Transformer，提升旅游需求预测精度。
A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting
- 生成虚拟样本模拟真实客流时空关系
- 相比传统Transformer，MASE降低18.37%
- 适合数据少的旅游管理场景
准确的旅游需求预测受限于历史数据不足及游客来源地间的复杂时空依赖。本文提出一种融合虚拟样本生成与新型Transformer预测器的框架，以应对数据稀缺问题。通过时空生成对抗网络（spatiotemporal GAN）利用图卷积网络动态建模空间相关性，生成逼真的虚拟样本；增强型Transformer结合因果卷积捕捉局部模式、自注意力机制建模长期依赖，摒弃自回归解码。采用联合训练策略，根据预测反馈优化虚拟样本生成，确保在数据有限条件下保持鲁棒性能。在真实世界日度与月度旅游需求数据集上的实验表明，该方法相较传统Transformer模型平均MASE降低18.37%，显著提升预测精度。该框架通过自适应时空样本增强与专用Transformer，有效解决旅游管理中的小样本预测难题。
原文摘要 · Abstract (English)
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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