Abstract: Fuel consumption data is imbalanced,which leads to the lower quality prediction interval.Aiming at this problem,an interval prediction model based on SMOTE-XGBoost algorithm is proposed.From the perspective of oversampling,the SMOTE algorithm is used to increase the number of minority samples in the training set,so that the imbalance of data in the training set is eliminated.For the interval prediction task,the quantile loss function is used as the loss function of the XGBoost algorithm.At the same time,by smoothing the small area around the origin of its first derivative,the quantile loss function is improved to solve the problem that the quantile loss function causes the tree in the XGBoost algorithm to not split.Based on the above work,the XGBoost algorithm and SMOTE algorithm are combined to train the interval prediction model,and finally the upper and lower bound of the prediction interval are obtained respectively.Conducting experiments based on the QAR data set,the experiment results indicate that compared with other methods,this method makes the prediction interval have higher interval coverage and narrower interval width,which improves the quality of the prediction interval.

Key words: Fuel consumption, Imbalanced data, Interval prediction, Quick Access Recorder(QAR) data, SMOTE, XGBoost