Prediction of the Dst Index with Bagging Ensemble-learning Algorithm

• Published in: The Astrophysical journal. Supplement series Vol. 248; no. 1; pp. 14 - 24
• Main Authors: Xu, S. B., Huang, S. Y., Yuan, Z. G., Deng, X. H., Jiang, K.
• Format: Journal Article
• Language: English
• Published: Saskatoon The American Astronomical Society 01.05.2020; IOP Publishing
• Subjects: Accuracy; Algorithms; Artificial neural networks; Bagging; Correlation coefficient; Correlation coefficients; DST Index; Electric fields; Ensemble learning; Geomagnetic activity; Geomagnetic fields; Geomagnetism; Machine learning; Magnetic fields; Magnetic storms; Model accuracy; Neural networks; Planetary magnetosphere; Plasma temperature; Root-mean-square errors; Solar wind; Solar wind parameters; Solar wind velocity; Space weather; Storm forecasting; Support vector machines; Wind speed

The Dst index is a commonly geomagnetic index used to measure the strength of geomagnetic activity. The accurate prediction of the Dst index is one of the main subjects of space weather studies. In this study, we use the Bagging ensemble-learning algorithm, which combines three algorithms-the artifi...