23 citations as recorded by crossref.

1. Development of multivariate ionospheric TEC forecasting algorithm using linear time series model and ARMA over low-latitude GNSS station D. Ratnam et al. https://doi.org/10.1016/j.asr.2018.03.024
2. Evaluation of the performance of DIAS ionospheric forecasting models I. Tsagouri https://doi.org/10.1051/swsc/2011110003
3. A new short-term forecasting model for the total electron content storm time disturbances I. Tsagouri et al. https://doi.org/10.1051/swsc/2018019
4. Comparison of GPS TEC variations with Holt-Winter method and IRI-2012 over Langkawi, Malaysia N. Elmunim et al. https://doi.org/10.1016/j.asr.2016.07.025
5. Empirical regional models for the short-term forecast of <i>M3000F2</i> during not quiet geomagnetic conditions over Europe M. Pietrella https://doi.org/10.5194/angeo-31-1653-2013
6. Forecast of global ionospheric TEC using an improved transformer model X. Wu et al. https://doi.org/10.1016/j.asr.2024.02.003
7. Solar activity impact on the Earth’s upper atmosphere I. Kutiev et al. https://doi.org/10.1051/swsc/2013028
8. Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using PCA-NN Method I. Mallika et al. https://doi.org/10.1109/JSTARS.2018.2877445
9. Short‐term forecasting of ionospheric total electron content over a low‐latitude global navigation satellite system station G. Sivavaraprasad & D. Ratnam https://doi.org/10.1049/iet-rsn.2017.0011
10. Ionospheric specification and forecasting based on observations from European ionosondes participating in DIAS project A. Belehaki et al. https://doi.org/10.2478/s11600-007-0010-x
11. The European Ionosonde Service: nowcasting and forecasting ionospheric conditions over Europe for the ESA Space Situational Awareness services A. Belehaki et al. https://doi.org/10.1051/swsc/2015026
12. A simplified HF radio channel forecasting model. Case study E. Moskaleva & N. Zaalov https://doi.org/10.1016/j.asr.2021.12.047
13. Ionospheric forecasts for the European region for space weather applications I. Tsagouri & A. Belehaki https://doi.org/10.1051/swsc/2015010
14. An Overview of Ionosphere—Thermosphere Models Available for Space Weather Purposes A. Belehaki et al. https://doi.org/10.1007/s11214-009-9510-0
15. Reliable prediction intervals with regression neural networks H. Papadopoulos & H. Haralambous https://doi.org/10.1016/j.neunet.2011.05.008
16. A method forfoF2short-term (1-24 h) forecast using both historical and real-timefoF2observations over European stations: EUROMAP model A. Mikhailov & L. Perrone https://doi.org/10.1002/2014RS005373
17. Ionospheric foF2 forecast over Europe based on an autoregressive modeling technique driven by solar wind parameters I. Tsagouri et al. https://doi.org/10.1029/2008RS004112
18. Forecasting global ionospheric TEC maps via ensemble learning with feature-engineered reconstruction K. XUE et al. https://doi.org/10.1016/j.cja.2025.104026
19. Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station G. Sivavaraprasad & D. Venkata Ratnam https://doi.org/10.1016/j.asr.2017.01.031
20. GNSS positioning error forecasting in the Arctic: ROTI and Precise Point Positioning error forecasting from solar wind measurements V. Fabbro et al. https://doi.org/10.1051/swsc/2021024
21. Progress in space weather modeling in an operational environment I. Tsagouri et al. https://doi.org/10.1051/swsc/2013037
22. An upgrade of the solar-wind-driven empirical model for the middle latitude ionospheric storm-time response I. Tsagouri & A. Belehaki https://doi.org/10.1016/j.jastp.2008.09.010
23. Predicting global ionospheric TEC maps using Gaussian process regression C. Shi et al. https://doi.org/10.1016/j.asr.2023.06.036