测绘学报 ›› 2023, Vol. 52 ›› Issue (8): 1235-1244.doi: 10.11947/j.AGCS.2023.20220052

• 大地测量学与导航 •    下一篇

融合VMD和XGBoost算法的GNSS高程时间序列预测方法

鲁铁定1,2, 李祯1, 贺小星3, 周世健4   

  1. 1. 东华理工大学测绘工程学院, 江西 南昌 330013;
    2. 自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室, 江西 南昌 330013;
    3. 江西理工大学土木与测绘工程学院, 江西 赣州 341000;
    4. 南昌航空大学, 江西 南昌 330063
  • 收稿日期:2022-01-27 修回日期:2022-11-16 发布日期:2023-09-07
  • 通讯作者: 李祯 E-mail:lizhenhd@163.com
  • 作者简介:鲁铁定(1974-),男,博士,教授,研究方向为测量数据处理。E-mail:tdlu@whu.edu.cn
  • 基金资助:
    国家自然科学基金(42061077;42064001;42104023);江西省自然科学基金(20202BABL213033;[JP]20202BAB212010);江西理工大学高层次人才科研启动项目(205200100564);2022年度中国科协科技智库青年人才计划

GNSS vertical time series prediction method integrating VMD and XGBoost algorithms

LU Tieding1,2, LI Zhen1, HE Xiaoxing3, ZHOU Shijian4   

  1. 1. School of Geodesy and Geomatics, East China University of Technology, Nanchang 330013, China;
    2. Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China;
    3. School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;
    4. Nanchang Hangkong University, Nanchang 330063, China
  • Received:2022-01-27 Revised:2022-11-16 Published:2023-09-07
  • Supported by:
    The National Natural Science Foundation of China (Nos. 42061077; 42064001; 42104023); The National Natural Science Foundation of Jiangxi, China (Nos. 20202BABL213033; 20202BAB212010); The Jiangxi University of Science and Technology High-level Talent Research Startup Project (No. 205200100564); Youth Talent Plan of Science and Technology Think Tank of China Association for Science and Technology in 2022

摘要: 针对传统GNSS高程时间序列预测模式存在特征选取不完善、稳定性差等问题,本文提出了一种融合VMD和XGBoost算法的预测模型。该模型通过多个VMD子模型得到重构信号,再将其作为特征输入XGBoost模型中进行原始时间序列的预测。为了验证预测模型的性能,试验选取4个观测站高程时间序列数据进行预测试验,试验结果表明,VMD模型能够准确地提取特征信息。与VMD-CNN-LSTM模型相比,VMD-XGBoost模型预测结果的MAE值降低了19.74%~35.90%,RMSE值降低了22.22%~31.14%,预测结果具有更高的稳定性且与原始时间序列呈较强相关性,可以较好地预测出目标时间序列。因此,该预测模型可应用于GNSS高程时间序列预测。

关键词: VMD, XGBoost, GNSS, 时间序列, 预测

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

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