北京测绘2026，Vol.40Issue(5)：587-593,7.DOI:10.19580/j.cnki.1007-3000.2025060028
GNSS时间序列多尺度分解与预测软件设计
Multi-scale decomposition and prediction software design for GNSS time series
摘要
Abstract
To address the cumbersome processing of global navigation satellite system(GNSS)time series data and the need for comparative analysis among multiple algorithms,a GNSS coordinate time series prediction software called GNSS_pre was designed,based on matrix laboratory(MATLAB).This software integrates decomposition models such as empirical mode decomposition(EMD)and prediction models including backpropagation(BP)neural networks.It constructs modules for time series input,decomposition,prediction,and accuracy evaluation,processing data through a standardized computa-tional workflow while offering data visualization,storage,and module expansion capabilities.Validation experiments using daily time series data from ten GNSS stations demonstrate that the software operates stably and reliably,with strong predic-tion accuracy,robustness,and fitting ability across various models.Specifically,the complete ensemble empirical mode decomposition with adaptive noise-extreme learning machine(CEEMDAN-ELM)model outperforms in four accuracy met-rics:root mean square error,mean absolute error,coefficient of determination,and mean bias error.The software's modu-lar design and reserved expansion slots provide new ideas and valuable references for flexible combinations of models in the field of GNSS time series prediction.关键词
全球卫星导航系统(GNSS)/时间序列/分解/预测模型/软件设计Key words
global navigation satellite system(GNSS)/time series/decomposition/prediction model/software design分类
天文与地球科学引用本文复制引用
周师睿,周宇,贺小星,田先清,廖渝,卢宗衡..GNSS时间序列多尺度分解与预测软件设计[J].北京测绘,2026,40(5):587-593,7.基金项目
国家级大学生创新创业项目(202410407026) （202410407026）
