Abstract
In this work, we develop gradient boosting machines (GBMs) for
forecasting the SYM-H index multiple hours ahead using different
combinations of solar wind and interplanetary magnetic field (IMF)
parameters, derived parameters, and past SYM-H values. Using Shapley
Additive Explanation (SHAP) values to quantify the contributions from
each input to predictions of the SYM-H index from GBMs, we show that our
predictions are consistent with physical understanding while also
providing insight into the complex relationship between the solar wind
and Earth’s ring current. In particular, we found that feature
contributions vary depending on the storm phase. We also perform a
direct comparison between GBMs and neural networks presented in prior
publications for forecasting the SYM-H index by training, validating,
and testing them on the same data. We find that the GBMs have a
comparable root mean squared error as the best published black-box
neural network schemes.