32 citations as recorded by crossref.

1. D s t Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. 10.1029/2019SW002215
2. The use of sequential recurrent neural filters in forecasting the <mml:math altimg="si3.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:msub><mml:mrow><mml:mstyle mathvariant="bold-italic"><mml:mi>D</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mi>s</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub></mml:math> index for the strong magnetic storm of autumn 2003 L. Ouarbya et al. 10.1016/j.aml.2011.12.002
3. DeepMAD: Deep Learning for Magnetic Anomaly Detection and Denoising X. Xu et al. 10.1109/ACCESS.2020.3006795
4. Prediction of theDstIndex and Analysis of Its Dependence on Solar Wind Parameters Using Neural Network A. Lethy et al. 10.1029/2018SW001863
5. Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks J. Uwamahoro et al. 10.5194/angeo-30-963-2012
6. A machine learning approach for automated ULF wave recognition G. Balasis et al. 10.1051/swsc/2019010
7. Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment J. Watermann et al. 10.1007/s11214-009-9494-9
8. A study on precursors leading to geomagnetic storms using artificial neural network G. SINGH & A. SINGH 10.1007/s12040-016-0702-1
9. Prediction of SYM-H index by NARX neural network from IMF and solar wind data L. Cai et al. 10.1007/s11431-009-0296-9
10. Potential role of energetic particle observations in geomagnetic storm forecasting D. Ameri & E. Valtonen 10.1016/j.asr.2019.05.012
11. Forecasting of global data-binning parameters for high-resolution empirical geomagnetic field models M. Sitnov et al. 10.1029/2012SW000783
12. AE index forecast at different time scales through an ANN algorithm based on L1 IMF and plasma measurements G. Pallocchia et al. 10.1016/j.jastp.2007.08.038
13. Statistical Prediction of Dst Index by Solar Wind Data and <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-Distributions P. Qin & R. Nishii 10.1109/TPS.2015.2485661
14. Analysis of geomagnetic hourly ranges D. Danskin & S. Lotz 10.1002/2015SW001184
15. Measuring Information Coupling between the Solar Wind and the Magnetosphere–Ionosphere System M. Stumpo et al. 10.3390/e22030276
16. On the existence of a long range correlation in the Geomagnetic Disturbance storm time (Dst) index A. Banerjee et al. 10.1007/s10509-011-0836-1
17. Forecast of the daily geomagnetic perturbations M. Stodilka 10.15407/knit2010.05.046
18. Empirical modeling of the storm time geomagnetic indices: a comparison between the local K and global Kp indices J. Uwamahoro & J. Habarulema 10.1186/1880-5981-66-95
19. Space Weather Effects Produced by the Ring Current Particles N. Ganushkina et al. 10.1007/s11214-017-0412-2
20. Comparison between three algorithms for Dst predictions over the 2003–2005 period E. Amata et al. 10.1016/j.jastp.2007.08.041
21. Geomagnetic storm forecasting service StormFocus: 5 years online T. Podladchikova et al. 10.1051/swsc/2018017
22. Comparison ofDstforecast models for intense geomagnetic storms E. Ji et al. 10.1029/2011JA016872
23. Dynamic Time Warping as a New Evaluation for Dst Forecast With Machine Learning B. Laperre et al. 10.3389/fspas.2020.00039
24. Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach M. Chandorkar et al. 10.1002/2017SW001627
25. Forbush Decreases and Geomagnetic Storms During a Highly Disturbed Solar and Interplanetary Period, 4–10 September 2017 B. Badruddin et al. 10.1029/2018SW001941
26. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale 10.1029/2018SW002061
27. Forecasting the Dst index using a swarm-optimized neural network J. Lazzús et al. 10.1002/2017SW001608
28. A comparative study of ionospheric IRIEup and ISP assimilative models during some intense and severe geomagnetic storms M. Pietrella et al. 10.1016/j.asr.2018.02.026
29. The Prediction of the Dst-Index Based on Machine Learning Methods A. Efitorov et al. 10.1134/S0010952518060035
30. Signatures of discrete scale invariance inDsttime series G. Balasis et al. 10.1029/2011GL048019
31. Extended geomagnetic storm forecast ahead of available solar wind measurements T. Podladchikova & A. Petrukovich 10.1029/2012SW000786
32. A cellular automata-based model of Earth's magnetosphere in relation withDstindex A. Banerjee et al. 10.1002/2014SW001138