大地测量与地球动力学2025，Vol.45Issue(8)：781-790,10.DOI:10.14075/j.jgg.2024.10.469
基于CEEMDAN与TCN-Attention的陆态网络GNSS高程时间序列多尺度预测
Multi-Scale Prediction of GNSS Elevation Time Series of CMONOC Based on CEEMDAN and TCN-Attention
摘要
Abstract
A multi-scale prediction model(referred to as C-TCN-A)based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and temporal convolutional network-attention mecha-nism(TCN-Attention)algorithms is proposed,which can be effectively applied to missing data imputation and future trend prediction of GNSS elevation time series.The model first employs CEEMDAN for multi-scale de-composition of time series,then utilizes TCN-Attention for prediction and reconstruction of different scale components to obtain final results.To validate the model's performance,12 observation stations were select-ed for 1-day and 5-day predictions,with comparisons made against multiple other models.Results demon-strate that in 1-day prediction,C-TCN-A reduces RMSE and MAE by 35％—40％and 36％—41％respec-tively while improving correlation coefficient R by 25％—29％.For 5-day prediction,it achieves reductions of 20％-26％in RMSE and 20％-28％in MAE,with R increasing by 26％-33％.To verify the model's universality,C-TCN-A was applied to 99 stations from the crustal movement observation network of China(CMONOC)for 1-day and 5-day predictions.Results indicate generally favorable RMSE and MAE metrics with concentrated error distribution,where most errors remain below 4 mm.Spatial analysis reveals regional performance differences,with optimal results achieved in the northwestern region.关键词
GNSS高程时间序列/陆态网络/改进经验模态分解/时间卷积网络Key words
GNSS elevation time series/CMONOC/improved empirical mode decomposition/temporal convolutional network分类
天文与地球科学引用本文复制引用
罗亦泳,占奥文,冯小欢..基于CEEMDAN与TCN-Attention的陆态网络GNSS高程时间序列多尺度预测[J].大地测量与地球动力学,2025,45(8):781-790,10.基金项目
国家自然科学基金(41861058). National Natural Science Foundation of China,No.41861058. （41861058）
