Machine Learning-based Prediction of Sunspots using Fourier Transform Analysis of the Time Series

José Víctor Rodríguez*, Ignacio Rodríguez-Rodríguez, Wai Lok Woo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The study of solar activity holds special importance since the changes in our star’s behavior affect both the Earth’s atmosphere and the conditions of the interplanetary environment. They can interfere with air navigation, space flight, satellites, radar, high-frequency communications, and overhead power lines, and can even negatively influence human health. We present here a machine learning-based prediction of the evolution of the current sunspot cycle (solar cycle 25). First, we analyze the Fourier Transform of the total time series (from 1749 to 2022) to find periodicities with which to lag this series and then add attributes (predictors) to the forecasting models to obtain the most accurate result possible. Consequently, we build a trained model of the series considering different starting points (from 1749 to 1940, with 1 yr steps), applying Random Forests, Support Vector Machines, Gaussian Processes, and Linear Regression. We find that the model with the lowest error in the test phase (cycle 24) arises with Random Forest and with 1915 as the start year of the time series (yielding a Root Mean Squared Error of 9.59 sunspots). Finally, for cycle 25 this model predicts that the maximum number of sunspots (90) will occur in 2025 March.

Original languageEnglish
Article number124201
Number of pages7
JournalPublications of the Astronomical Society of the Pacific
Issue number1042
Publication statusPublished - 19 Dec 2022


Dive into the research topics of 'Machine Learning-based Prediction of Sunspots using Fourier Transform Analysis of the Time Series'. Together they form a unique fingerprint.

Cite this