Prediction of Vehicle Registration Quantity Based on ARIMA Model
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Abstract
In this paper, we mainly studies the automobile registration quantity problem, which has obvious periodicity and time series characteristics. We used ARIMA model to predict the features of the monthly mean, then extracted the feature values, using the XGBoost training model.By comparing the linear regression model, the random tree forest model and the XGBoost model, an appropriate deviation factor of the real value and the predicted value of the vehicle registration amount of the model was selected to get the final result. The predicted results can accurately describe the changing trend of the number of car registrations, and provide reference for relevant departments to plan roads, arrange vehicles for travel, and provide efficient guidance for travel.
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