Prévision de la demande touristique avec imagerie de séries chronologiques : un modèle d'apprentissage profond
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.
Jian-Wu Bi, Hui Li, Zhi-Ping Fan
► Tourism demand forecasting with time series imaging: A deep learning model (sur Science Direct)