TY - JOUR
T1 - Machine Learning-based Prediction of Sunspots using Fourier Transform Analysis of the Time Series
AU - Rodríguez, José Víctor
AU - Rodríguez-Rodríguez, Ignacio
AU - Lok Woo, Wai
PY - 2022/12/19
Y1 - 2022/12/19
N2 - 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.
AB - 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.
KW - Support vector machine
KW - Processes regression
KW - Sunspots
KW - Solar activity
KW - Time series analysis
KW - Gaussian
KW - Linear regression
KW - Random Forests
UR - http://www.scopus.com/inward/record.url?scp=85145288216&partnerID=8YFLogxK
U2 - 10.1088/1538-3873/aca4a3
DO - 10.1088/1538-3873/aca4a3
M3 - Article
AN - SCOPUS:85145288216
SN - 0004-6280
VL - 134
JO - Publications of the Astronomical Society of the Pacific
JF - Publications of the Astronomical Society of the Pacific
IS - 1042
M1 - 124201
ER -