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Tourism demand forecasting with time series imaging: A deep learning model

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  • Bi, Jian-Wu
  • Li, Hui
  • Fan, Zhi-Ping

Abstract

To leverage computer vision technology to improve the accuracy of tourism demand forecasting, a model based on deep learning with time series imaging is proposed. The model consists of three parts: sequence image generation, image feature extraction, and model training. In the first part, the tourism demand data are encoded into images. In the second part, the convolution and pooling layers are used to extract features from the obtained images. In the final part, the extracted features are input into long short-term memory networks. Based on historical tourism demand data, the model for forecasting future tourism demand can be obtained. The performance of the proposed model is experimentally assessed through comparing against seven benchmark models.

Suggested Citation

  • Bi, Jian-Wu & Li, Hui & Fan, Zhi-Ping, 2021. "Tourism demand forecasting with time series imaging: A deep learning model," Annals of Tourism Research, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:anture:v:90:y:2021:i:c:s016073832100133x
    DOI: 10.1016/j.annals.2021.103255
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    References listed on IDEAS

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    4. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
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    8. Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2023. "Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies," Papers 2302.10175, arXiv.org.
    9. Ming Yin & Feiya Lu & Xingxuan Zhuo & Wangzi Yao & Jialong Liu & Jijiao Jiang, 2024. "Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 344-365, March.
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