Multilevel Residual Prophet Network Time Series Model for Prediction of Irregularities on High-Speed Railway Track

Accurate prediction of the change in track irregularity plays an essential role in keeping the high-speed railway safe and stable. Regular maintenance is an important measure to guarantee track smoothness, which has a great influence on the change of track state. This paper introduces the time series anomaly detection model (AD) to detect changes by tracking the difference between the mean values of two sliding time windows. These changes are taken as one of the holidays of the prophet model which is called the AD_ Prophet model. Moreover, this paper proposes a multilevel residual prophet (Re-Prophet) prediction network which can make full use of the information of residual data to predict the results. The final predicted result is the sum of the value of each prediction. To explore the characteristics of the time series, the Hodrick-Prescott filter is used to explore the trend. A multi-month-wise box plot is used to explore the seasonal volatility and the wavelet transform is used to explore the cyclical variation. Based on these characteristics, the fitting model can be chosen. Finally, to verify the high accuracy of the model, the surface irregularities of the high-speed railway are used as the research objects. Compared with conventional prediction algorithms, the proposed model has the smallest prediction deviation and the highest accuracy based on the evaluation indicators such as mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error.