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Tourism Demand Forecasting: a Deep Learning Model Based on Spatial-temporal Transformer by Jiaying Chen; Cheng Li; Liyao Huang; Weimin Zheng is a Social Sciences article available to read on EtoBox.

What is Tourism Demand Forecasting: a Deep Learning Model Based on Spatial-temporal Transformer about?

## Purpose Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects. ## Design/methodology/approach A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts. ## Findings The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for

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Author

Jiaying Chen; Cheng Li; Liyao Huang; Weimin Zheng

Publisher

Emerald

Published

2023

Field

Social Sciences