SVM-XGBoost-based railway passenger turnover prediction method

The invention relates to the technical field of data prediction, in particular to a railway passenger turnover quantity prediction method based on SVM-XGBoost, which fully considers sudden public health factors, selects indexes such as the number of existing pneumonia definite cases as influence factors, and predicts the railway passenger turnover quantity by using an SVM-XGBoost combined model. And a prediction result is compared with an SARIMA model and an exponential smoothing model in a time sequence method, which shows that the method has a better prediction effect under the influence of pneumonia. According to the method, the combination model based on SVM-XGBoost and the prediction model based on the time sequence are different when the railway passenger turnover quantity is predicted, the combination prediction model shows higher prediction capacity in the face of sudden public health problems, and the overall prediction capacity is high. 本发明涉及数据预测技术领域，具体涉及基于SVM-XGBoost的铁路旅客周转量预测方法，本发明充分考虑了突发性公共卫生因素，选