29 citations as recorded by crossref.

1. Principal components and iterative regression analysis of geophysical series: Application to Sunspot number (1750–2004) D. Nordemann et al. https://doi.org/10.1016/j.cageo.2007.09.022
2. Prediction of sunspot number amplitude and solar cycle length for cycles 24 and 25 N. Rigozo et al. https://doi.org/10.1016/j.jastp.2010.09.005
3. Solar Weather Event Modelling and Prediction M. Messerotti et al. https://doi.org/10.1007/s11214-009-9574-x
4. A long-term trend in the F2-layer critical frequency as observed at Alma-Ata ionosonde station G. Gordiyenko et al. https://doi.org/10.1186/1880-5981-66-125
5. Prediction of the annual maximum sunspot number for Solar Cycles 22, 23, 24 and 25 via the geomagnetic activity indices aa and Ap M. Mohan & R. Patel https://doi.org/10.1007/s10509-026-04573-5
6. Solar Cycle Predictions (Invited Review) W. Pesnell https://doi.org/10.1007/s11207-012-9997-5
7. Predicting Sunspot Numbers Based on Inverse Number and Intelligent Fixed Point Z. Liu et al. https://doi.org/10.1007/s11207-021-01835-z
8. Multi-resolution analysis of global surface air temperature and solar activity relationship M. Souza Echer et al. https://doi.org/10.1016/j.jastp.2008.09.032
9. Prediction of solar cycle 24 and beyond K. Hiremath https://doi.org/10.1007/s10509-007-9728-9
10. Long-Term Sunspot Number Prediction based on EMD Analysis and AR Model T. Xu et al. https://doi.org/10.1088/1009-9271/8/3/10
11. Progress in Solar Cycle Predictions: Sunspot Cycles 24–25 in Perspective D. Nandy https://doi.org/10.1007/s11207-021-01797-2
12. Predicting the Amplitude of a Solar Cycle Using the North – South Asymmetry in the Previous Cycle: II. An Improved Prediction for Solar Cycle 24 J. Javaraiah https://doi.org/10.1007/s11207-008-9269-6
13. Forecasting Solar Cycle 24 using the relationship between cycle length and maximum sunspot number S. Watari https://doi.org/10.1029/2008SW000397
14. Predicting maximum sunspot number in solar cycle 24 N. Bhatt et al. https://doi.org/10.1007/s12036-009-0004-y
15. Solar Cycle Prediction Using Precursors and Flux Transport Models R. Cameron & M. Schussler https://doi.org/10.1086/512049
16. Statistical method to predict the sunspots number H. Abdel-Rahman & B. Marzouk https://doi.org/10.1016/j.nrjag.2018.08.001
17. Are solar cycles predictable? M. Schüssler https://doi.org/10.1002/asna.200710836
18. On Prediction of the Strength of the 11-Year Solar Cycle No. 24 V. Obridko & B. Shelting https://doi.org/10.1007/s11207-008-9138-3
19. A Preliminary Estimate of the Size of the Coming Solar Cycle 24, based on Ohl’s Precursor Method R. Kane https://doi.org/10.1007/s11207-007-0475-4
20. Predictions of Solar Cycle 24 W. Pesnell https://doi.org/10.1007/s11207-008-9252-2
21. Prediction of solar minimum and maximum epochs on the basis of spectral characteristics for the next millennium N. Rigozo et al. https://doi.org/10.1016/j.pss.2010.09.020
22. Predicting Solar Cycle 24 and beyond M. Clilverd et al. https://doi.org/10.1029/2005SW000207
23. Sun's retrograde motion and violation of even-odd cycle rule in sunspot activity J. Javaraiah https://doi.org/10.1111/j.1365-2966.2005.09403.x
24. Solar Cycle Predictions Based on Extrapolation of Spectral Components: An Update R. Kane https://doi.org/10.1007/s11207-007-9059-6
25. Trend estimation and univariate forecast of the sunspot numbers: Development and comparison of ARMA, ARIMA and Autoregressive Neural Network models S. Chattopadhyay et al. https://doi.org/10.1016/j.crte.2011.07.008
26. Characteristics of probability distribution functions of low- and high-latitude current systems during Solar Cycle 24 B. Kakad & A. Kakad https://doi.org/10.1016/j.asr.2019.12.021
27. Simplex Projection Predictions of the Remainder of Solar Cycle 25 and the Next Solar Cycle 26 Based on the Monthly Mean Sunspot Numbers K. Gerçeker et al. https://doi.org/10.1007/s11207-025-02577-y
28. Sunspot cycle prediction using Warped Gaussian process regression Í. Gonçalves et al. https://doi.org/10.1016/j.asr.2019.11.011
29. An Improved Prediction of Solar Cycle 25 Using Deep Learning Based Neural Network A. Prasad et al. https://doi.org/10.1007/s11207-023-02129-2