GNSS vertical time series prediction method integrating VMD and XGBoost algorithms

doi:10.11947/j.AGCS.2023.20220052

Abstract

Abstract: Aiming at the problems of imperfect feature selection and poor stability in traditional GNSS elevation time series prediction models, a combined forecasting model based on variational mode decomposition (VMD) and extreme gradient boosting (XGBoost) algorithm is proposed. The model obtains the reconstructed signal through multiple VMD sub-models, and inputs it into the XGBoost model as a feature for forecasting of the original time series. To verify the performance of the forecasting model, the experiment selects the vertical time series data of 4 observatories for the forecasting experiment, the experimental results show that the VMD model can accurately extract the features. Compared with the VMD-CNN-LSTM model, the experimental results of VMD-XGBoost show that the MAE values are reduced by 19.74%~35.90% and the RMSE values are reduced by 22.22%~31.14%. The forecasting results have higher stability and are highly correlated to the original time series, which can better predict the Targeted time series. Therefore, the forecasting method can be applied to GNSS vertical time series forecasting.

Key words: VMD, XGBoost, GNSS, time series, forecasting

CLC Number:

P228

LU Tieding, LI Zhen, HE Xiaoxing, ZHOU Shijian. GNSS vertical time series prediction method integrating VMD and XGBoost algorithms[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1235-1244.

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