Filling missing values of multi-station GNSS coordinate time series based on matrix completion

Bao, Zhi When; Chang, Guobin; Zhang, Laihong; Chen, Guoliang; Zhang, Siyu

doi:10.1016/j.measurement.2021.109862

Cited by 15 publications

(2 citation statements)

References 31 publications

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“…GNSS provides critical daily position time series for geodetic and geophysical studies. However, due to unforeseeable factors such as receiver malfunctions, human errors, or deteriorating environmental conditions, the occurrence of missing data is inevitable (Bao et al, 2021). The presence of missing data can significantly impact time series analysis.…”

Section: Impact Of Missing Data On Adaptive Eemdmentioning

confidence: 99%

Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

Li,

Guo

2023

Preprint

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Abstract. Ensemble Empirical Mode Decomposition (EEMD) has been widely used in the data analysis. Adaptive EEMD further improves computational efficiency through the adaptability in the white noise amplitude and set average number. However, its effectiveness of the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding on the inevitable missing data and offsets issues have not been comprehensively validated. It is verified with 5- year time series through 300 simulations for each case. The results show that high accuracy could reach for the overall random missing rate below 15 % and avoiding consecutive missing epochs exceeding 30. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of vertical component of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network, demonstrates the advantage in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients, power spectral density (PSD) indices and signal noise ratio (SNR), the means for adaptive EEMD are 0.36, -0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, -0.50 and 0.23. Meanwhile, significance test of the residuals further substantiate the effectiveness in periodic signal extraction, which shows there is no annual signal remained. Also, the longer the series, the higher accuracy of extracted periodic signal is reasonable concluded via the significance test. Furthermore, the time-varying periodic characteristics is more conducive to analyze the driving factors. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on the proper dealing with missing data and offsets.

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Section: Impact Of Missing Data On Adaptive Eemdmentioning

confidence: 99%

Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

Li,

Guo

2023

Preprint

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“…Zhang et al [18] evaluated the performance of the missForest (a machine learning method), Cubic spline, orthogonal polynomial, RegEM, and Hermite methods applied to the interpolation of GPS time series, showing that the performance of missForest was superior to the four traditional interpolation methods. Bao et al [19] proposed a matrix completion technique based on a singular value thresholding algorithm for interpolation of the GNSS position time series, which is a spatio-temporal interpolation method that showed satisfactory performance in experiments. Qiu et al [20] proposed an iteration empirical mode decomposition (Iteration EMD) method for interpolation of the GNSS position time series, and the experimental result showed that Iteration EMD can preserve a variance of 75.9% with the first three principal components, higher than 66.5% for the interpolation EMD.…”

Section: Introductionmentioning

confidence: 99%

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

Li,

Lu,

et al. 2023

Remote Sensing

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The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.

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Dynamic Imputation Methodology for Multi-source Streaming Mobility Data

Dhont

Tsiporkova

Gonzaléz-Deleito

2022

Smart Innovation, Systems and Technologies

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Filling missing values of multi-station GNSS coordinate time series based on matrix completion

Cited by 15 publications

References 31 publications

Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

Extraction of periodic signals in GNSS vertical coordinate time series using adaptive Ensemble Empirical Modal Decomposition method

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

Dynamic Imputation Methodology for Multi-source Streaming Mobility Data

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