10 citations as recorded by crossref.

1. Dst Index Forecast Based on Ground‐Level Data Aided by Bio‐Inspired Algorithms J. Lazzús et al. https://doi.org/10.1029/2019SW002215
2. The Earth’s Magnetosphere: A Systems Science Overview and Assessment J. Borovsky & J. Valdivia https://doi.org/10.1007/s10712-018-9487-x
3. Improving Disturbance Storm Time Index Prediction Using Linear and Nonlinear Parametric Models: A Comprehensive Analysis M. Jawad et al. https://doi.org/10.1109/TPS.2018.2887202
4. A Nonextensive Entropy Approach to Solar Wind Intermittency M. Leubner & Z. Voros https://doi.org/10.1086/425893
5. Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review S. Raubitzek & T. Neubauer https://doi.org/10.3390/e23121672
6. The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting E. Camporeale https://doi.org/10.1029/2018SW002061
7. A fractal interpolation approach to improve neural network predictions for difficult time series data S. Raubitzek & T. Neubauer https://doi.org/10.1016/j.eswa.2020.114474
8. Taming the Chaos in Neural Network Time Series Predictions S. Raubitzek & T. Neubauer https://doi.org/10.3390/e23111424
9. Exploration of a Composite Index to Describe Magnetospheric Activity: Reduction of the Magnetospheric State Vector to a Single Scalar J. Borovsky & M. Denton https://doi.org/10.1029/2018JA025430
10. Forecasting the Dst index using a swarm‐optimized neural network J. Lazzús et al. https://doi.org/10.1002/2017SW001608