Prediction of daily tourism volume based on maximum correlation minimum redundancy feature selection and long short‐term memory network

Historical tourism volume, search engine data, and weather calendar data have close causal relationship with daily tourism volume. However, when used in the prediction of daily tourism volume, the feature variables of the huge and complex search engine data do not have strong independence. These repetitive and highly relevant data must be analyzed and selected; otherwise, they will increase the training burden of neural network and reduce the prediction effect. This study proposes a daily tourism volume prediction model, maximum correlation minimum redundancy feature selection and long short‐term memory, on the basis of feature selection and deep learning. Firstly, the multivariate high‐dimensional features, including search engine data and weather factors, are selected to identify the key influencing factors. Secondly, the deep neural network is used to make a multistep forward rolling prediction of daily tourism volume. Results show that keywords of famous scenic spots, weather, historical tourism volume, and tourism strategies in the search engine data significantly improve the prediction accuracy of daily tourism volume. The proposed maximum correlation minimum redundancy feature selection and long short‐term memory model performs better than other models, such as autoregressive integrated moving average, multiple regression, support vector machine, and long short‐term memory.