基于Prophet-XGBoost模型的GNSS高程时间序列预测

Prediction of GNSS Vertical Coordinate Time Series Based on Prophet-XGBoost Model

针对GNSS时间序列非平稳性和非线性等特点,通过分析XGBoost模型与Prophet模型的适用性与特点,构建Prophet-XGBoost预测模型。该模型先通过Prophet模型对GNSS原始时间序列进行分解,然后通过XGBoost模型进行分部预测,等权相加得到预测结果。实验选用ALGO、ALRT、BRST三个IGS站U分量日坐标时间序列数据,采用MAE和RMSE作为评价指标。结果表明,与单一的XGBoost模型和Prophet模型相比,Prophet-XGBoost模型的MAE和RMSE值均得到一定程度优化,说明该模型具备有效性,可用于GNSS时间序列预测。

According to the non-stationary and nonlinear characteristics of GNSS time series,we analyze the applicability and characteristics of XGBoost and Prophet models,and construct a Prophet-XGBoost prediction model.Firstly,using the Prophet model,we decompose the GNSS original time series,then carry out partial prediction by XGBoost model.We obtain prediction results by equal weight addition.We select the daily coordinate time series data of U component of ALGO,ALRT and BRST IGS stations in the experiment,and use MAE and RMSE as evaluation indexes.The experimental results show that compared with the single XGBoost model and Prophet model,the MAE and RMSE values of Prophet-XGBoost model are optimized to a certain extent.The effectiveness of this method is verified and can be used for GNSS time series prediction.