Detecting outliers in GNSS position time series using machine learning techniques

Abstract

The Global Navigation Satellite System (GNSS) position time series is
applied in studies that require high-precision positioning, such as
monitoring tectonic movements and Earth deformation. Outliers in GNSS
position time series can significantly impact the accuracy of station
positioning and movement parameters, leading to distorted data analysis
outcomes. This study investigates the effectiveness of three machine
learning techniques, including-Isolation Forest, One-Class Support Vector
Machines (O-C SVM), and Local Outlier Factor (LOF) for outlier detection
in GNSS position time series, with a specific focus on the SYNT model
where outliers account for a substantial proportion (15%). Through
comprehensive analysis, our results highlight the exceptional
performance of the Isolation Forest method. It demonstrates remarkable
accuracy in identifying outliers, effectively detecting the majority of them,
and achieving an area under the ROC curve close to 1. In contrast, the LOF
method performs less effectively in outlier detection, while the O-C SVM
method displays relatively higher accuracy in identifying normal data
points. These findings emphasize the significant advantages of leveraging
machine learning approaches in processing continuous GNSS
measurement data. By effectively identifying and handling outliers, these
techniques enhance the accuracy and reliability of data analysis in GNSS
position time series, ultimately establishing their superiority in the field of
data analysis