Abstract

The potential is investigated of predicting the time series of the Dst geomagnetic index using various adaptive methods: artificial neural networks (classical multilayer perceptrons), decision trees (random forest), gradient boosting. The prediction is based on the parameters of the solar wind and interplanetary magnetic field measured at the Lagrange point L1 in the ACE spacecraft experiment. It is shown that the best prediction skill of the three adaptive methods is demonstrated by gradient boosting.

中文翻译：

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使用自适应方法预测 Dst 地磁指数

摘要

研究了使用各种自适应方法预测 Dst 地磁指数时间序列的潜力：人工神经网络（经典多层感知器）、决策树（随机森林）、梯度提升。该预测基于 ACE 航天器实验中在拉格朗日点 L1 处测量的太阳风和行星际磁场参数。结果表明，梯度提升证明了三种自适应方法的最佳预测技巧。