TY  - JOUR
A1  - Zhelayskaya, Irina S.
A1  - Vasile, Ruggero
A1  - Shprits, Yuri Y.
A1  - Stolle, Claudia
A1  - Matzka, Jürgen
T1  - Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index
T2  - Space Weather: The International Journal of Research and Applications
N2  - The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.
KW  - Kp index
KW  - Predictive models
KW  - Feature selection
KW  - Machine learning
KW  - Validation
Y1  - 2019
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/48070
SN  - 1542-7390
VL  - 17
IS  - 10
SP  - 1461
EP  - 1486
PB  - American Geophysical Union
CY  - Washington
ER  -