Sunspot cycle prediction using Warped Gaussian process regression
Abstract
Solar cycle prediction is a key activity in space weather research. Several techniques have been employed in recent decades in order to try to forecast the next sunspot-cycle maxima and time. In this work, the Gaussian process, a machine-learning technique, is used to make a prediction for the solar cycle 25 based on the annual sunspot number 2.0 data from 1700 to 2018. A variation known as Warped Gaussian process is employed in order to deal with the non-negativity constraint and asymmetrical data distribution. Tests using holdout data yielded a root mean square error of 10.0 within 5 years and 25.0-35.0 within 10 years. Simulations using the predictive distribution were performed to account for the uncertainty in the prediction. Cycle 25 is expected to last from 2019 to 2029, with a peak sunspot number about 117 (110 by the median) occurring most likely in 2024. Thus our method predicts that solar Cycle 25 will be weaker than previous ones, implying a continuing trend of declining solar activity as observed in the past two cycles.
- Publication:
-
Advances in Space Research
- Pub Date:
- January 2020
- DOI:
- 10.1016/j.asr.2019.11.011
- Bibcode:
- 2020AdSpR..65..677G
- Keywords:
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- Sunspot number;
- Solar cycle;
- Machine learning;
- Gaussian process