1. Alghushairy O., Alsini R., Soule T., et al. A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams // Big Data and Cognitive Computing. — 2020. — Vol. 5, no. 1. — https://doi.org/10.3390/bdcc5010001.
2. Altamimi Z., Rebischung P., Collilieux X., et al. ITRF2020: an augmented reference frame refining the modeling of nonlinear station motions // Journal of Geodesy. — 2023. — Vol. 97, no. 5. — https://doi.org/10.1007/s00190-023-01738-w.
3. Altamimi Z., Rebischung P., Metivier L., et al. ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions // Journal of Geophysical Research: Solid Earth. — 2016. — Vol. 121, no. 8. — P. 6109–6131. — https://doi.org/10.1002/2016jb013098.
4. Argus D. F., Gordon R. G. and DeMets C. Geologically current motion of 56 plates relative to the no-net-rotation reference frame: NNR-MORVEL56 // Geochemistry, Geophysics, Geosystems. — 2011. — Vol. 12, no. 11. — P. 1–13. — https://doi.org/10.1029/2011gc003751.
5. Blazquez-Garciá A., Conde A., Mori U., et al. A Review on Outlier/Anomaly Detection in Time Series Data // ACM Computing Surveys. — 2021. — Vol. 54, no. 3. — P. 1–33. — https://doi.org/10.1145/3444690.
6. Blewitt G., Hammond W. C. and Kreemer C. Harnessing the GPS Data Explosion for Interdisciplinary Science // Eos. — 2018. — Vol. 99. — https://doi.org/10.1029/2018eo104623.
7. Blewitt G., Kreemer C., Hammond W. C., et al. MIDAS robust trend estimator for accurate GPS station velocities without step detection // Journal of Geophysical Research: Solid Earth. — 2016. — Vol. 121, no. 3. — P. 2054–2068. — https://doi.org/10.1002/2015jb012552.
8. Bock Y., Moore A. W., Argus D., et al. Extended Solid Earth Science ESDR System (ES3): Algorithm Theoretical Basis Document, NASA MEaSUREs Project. — SOPAC/CSRC Archive, 2023.
9. Bracewell R. Heaviside’s Unit Step Function, H(x) // The Fourier Transform and Its Applications. — New York : McGrawHill, 2000. — P. 61–65.
10. Crocetti L., Schartner M. and Soja B. Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning // Remote Sensing. — 2021. — Vol. 13, no. 19. — P. 3906. — https://doi.org/10.3390/rs13193906.
11. Fritsch F. N. and Carlson R. E. Monotone Piecewise Cubic Interpolation // SIAM Journal on Numerical Analysis. — 1980. — Vol. 17, no. 2. — P. 238–246.
12. Gabsatarov Yu. V. Analysis of deformation processes in the lithosphere from geodetic measurements based on the example of the San Andreas fault // Geodynamics & Tectonophysics. — 2012. — Vol. 3, no. 3. — P. 275–287. — https://doi.org/10.5800/gt-2012-3-3-0074.
13. Gazeaux J., Williams S., King M., et al. Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment // Journal of Geophysical Research: Solid Earth. — 2013. — Vol. 118, no. 5. — P. 2397–2407. — https://doi.org/10.1002/jgrb.50152.
14. Gitis V., Derendyaev A. and Petrov K. Analyzing the Performance of GPS Data for Earthquake Prediction // Remote Sensing. — 2021. — Vol. 13, no. 9. — P. 1842. — https://doi.org/10.3390/rs13091842.
15. Gvishiani A. D., Dobrovolsky M. N., Dzeranov B. V., et al. Big Data in Geophysics and Other Earth Sciences // Izvestiya, Physics of the Solid Earth. — 2022. — Vol. 58. — P. 1–29. — https://doi.org/10.1134/s1069351322010037.
16. Ji K., Shen Y. and Wang F. Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis // Remote Sensing. — 2020. — Vol. 12, no. 6. — P. 992. — https://doi.org/10.3390/rs12060992.
17. Liu F. T., Ting K. M. and Zhou Z.-H. Isolation Forest // 2008 Eighth IEEE International Conference on Data Mining. — Pisa, Italy : IEEE, 2008. — P. 413–422. — https://doi.org/10.1109/icdm.2008.17.
18. Liu T. and Kossobokov V. G. Displacements Before and After Great Earthquakes: Geodetic and Seismic Viewpoints // Pure and Applied Geophysics. — 2021. — Vol. 178, no. 4. — P. 1135–1155. — https://doi.org/10.1007/s00024-021-02694-2.
19. Londschien M., Bühlmann P. and Kovács S. Random Forests for Change Point Detection // Journal of Machine Learning Research. — 2023. — Vol. 24. — P. 1–45.
20. Nikolaidis R. Observation of Geodetic and Seismic Deformation with the Global Positioning System: Ph.D. Thesis. — San Diego : University of California, 2002. — 265 p.
21. Ozbey V., Ergintav S. and Tari E. GNSS Time Series Analysis with Machine Learning Algorithms: A Case Study for Anatolia // Remote Sensing. — 2024. — Vol. 16, no. 17. — P. 3309. — https://doi.org/10.3390/rs16173309.
22. Steblov G. M., Kogan M. G., Levin B. V., et al. Spatially linked asperities of the 2006-2007 great Kuril earthquakes revealed by GPS // Geophysical Research Letters. — 2008. — Vol. 35, no. 22. — https://doi.org/10.1029/2008gl035572.
23. Truong C., Oudre L. and Vayatis N. Selective review of offline change point detection methods // Signal Processing. — 2020. — Vol. 167. — P. 107299. — https://doi.org/10.1016/j.sigpro.2019.107299.
24. Xue X. and Freymueller J. T. Machine Learning for Single-Station Detection of Transient Deformation in GPS Time Series With a Case Study of Cascadia Slow Slip // Journal of Geophysical Research: Solid Earth. — 2023. — Vol. 128, no. 2. — https://doi.org/10.1029/2022jb024859.
25. Yamaga N. and Mitsui Y. Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 TohokuOki Earthquake Based on Recurrent Neural Network // Geophysical Research Letters. — 2019. — Vol. 46, no. 21. — P. 11886–11892. — https://doi.org/10.1029/2019gl084578.
26. Zhang S., Gong L., Zeng Q., et al. Imputation of GPS Coordinate Time Series Using missForest // Remote Sensing. — 2021. — Vol. 13, no. 12. — P. 2312. — https://doi.org/10.3390/rs13122312.