| 引用本文: | 鲁铁定,陶蕊,贺小星,等.顾及噪声影响的GNSS高程序列预测Prophet方法.[J].国防科技大学学报,2023,45(2):121-130.[点击复制] |
| LU Tieding,TAO Rui,HE Xiaoxing,et al.Prophet method of GNSS vertical time series prediction considering the influence of noise[J].Journal of National University of Defense Technology,2023,45(2):121-130[点击复制] |
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| 顾及噪声影响的GNSS高程序列预测Prophet方法 |
| 鲁铁定1,陶蕊1,贺小星2,程远明3,周子琪1 |
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(1. 东华理工大学 测绘工程学院, 江西 南昌 330013;2. 江西理工大学 土木与测绘工程学院, 江西 赣州 341000;3. 南昌市城市规划设计研究总院, 江西 南昌 330200)
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| 摘要: |
| 全球导航卫星系统(global navigation satellite system, GNSS)高程时间序列具有非平稳、非线性、含噪声等特点,在深入研究Prophet预测模型的基础上,针对Prophet预测模型对于趋势信号和周期信号有良好预测效果这一特性,提出一种引入经验模态分解(empirical mode decomposition,EMD)的“降噪—分解—预测”组合GNSS高程时间序列预测方法。该方法先将原始时间序列进行EMD降噪,再对降噪后的序列进行分解预测,最后重构各分量预测信号为最终预测序列。通过对实测高程数据进行研究,实验结果表明:降噪后信号的平均信噪比为10.30dB,能量百分比平均为88.75%；利用所构建的短期预测方法,GNSS高程时间序列预测结果的均方根误差分别平均提升26.41%和14.88%；平均百分比误差分别平均提升18.92%和7.91%,验证了组合预测方法的有效性及实用性。 |
| 关键词: Prophet 经验模态分解 降噪 时间序列预测 组合模型 |
| DOI：10.11887/j.cn.202302014 |
| 投稿日期：2021-04-23 |
| 基金项目:国家自然科学基金资助项目(42061077,42064001,42104023)；国家重点研发计划资助项目(2016YFB0501405)；江西省自然科学基金资助项目(2017BAB203032)；江西理工大学高层次人才科研启动资助项目(2021205200100564) |
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| Prophet method of GNSS vertical time series prediction considering the influence of noise |
| LU Tieding1, TAO Rui1, HE Xiaoxing2, CHENG Yuanming3, ZHOU Ziqi1 |
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(1. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China;2. School of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;3. Nanchang Urban Planning & Design Institute, Nanchang 330200, China)
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| Abstract: |
| GNSS (global navigation satellite system) vertical time series have the characteristics of non-stationary, non-linear, and noisy. Based on the in-depth study of the Prophet prediction model, and the good predictive effect of Prophet prediction model on trend signals and periodic signals, a “noise reduction-decomposition-prediction” combined prediction method of GNSS vertical time series that introduces EMD (empirical mode decomposition) was proposed. EMD denoising was performed on the original time series, the denoised series were decomposed and predicted, and the predicted signal of each component was reconstructed into the final predicted series. The measured vertical data was used for research, and results show that the average signal-to-noise ratio of the signal after noise reduction is 10.30 dB, and the average energy percentage is 88.75%; using the short-term prediction method, the root-mean-square errors of GNSS vertical time series prediction results are increased by 26.41% and 14.88% on average, respectively; the average percentage errors are increased by 18.92% and 7.91% on average, respectively, and the effectiveness and practicability of the combined forecasting method are verified. |
| Keywords: Prophet empirical mode decomposition noise reduction time series prediction combined model |
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