Bibliographic Details
| Title: |
TS_Predictor: a deep learning toolbox for GNSS time series prediction with signal decomposition and nonlinear modelling. |
| Authors: |
He, Xiaoxing1 (AUTHOR) xxh@jxust.edu.cn, Zhou, Yu2 (AUTHOR), Li, Jun2 (AUTHOR), Kermarrec, Gaël3 (AUTHOR), Fernandes, Rui4 (AUTHOR), Montillet, Jean-Philippe4 (AUTHOR), Zhang, Shuangcheng2 (AUTHOR), Hu, Shunqiang5 (AUTHOR) |
| Source: |
Advances in Space Research. Jun2026, Vol. 77 Issue 12, p11566-11575. 10p. |
| Subject Terms: |
*Deep learning, *Artificial satellites in navigation, *Nonlinear statistical models, *Forecasting, *Signal separation, *Data transformations (Statistics) |
| Reviews & Products: |
MatLab (Computer software) |
| Abstract: |
Predicting Global Navigation Satellite System coordinate time series is essential for early warning systems, infrastructure monitoring, and geophysical modeling, yet no dedicated open-source tool currently exists for this task. We developed TS_Predictor, a MATLAB toolbox that brings together deep learning, signal decomposition and nonlinear modelling in a single, accessible package. Providing a complete workflow from raw data preprocessing to prediction accuracy assessment. The software supports multiple data formats from major data centers, implements comprehensive preprocessing functions including missing data interpolation, outlier detection, offset correction, and common mode error removal, and offers six decomposition methods combined with six prediction models. We introduce a novel Weighted Quality Evaluation index (WQE) that combines root mean square error, mean absolute error, symmetric mean absolute percentage error, and coefficient of determination into a unified metric for model comparison. Validation using data from ten stations in Yunnan, China (2010 to 2025) demonstrates that the decomposition-prediction approach consistently outperforms direct prediction. TS_Predictor is freely available at https://github.com/SpaceGeodesyLab/TS_Predictor. [ABSTRACT FROM AUTHOR] |
| Database: |
Academic Search Index |