Dst Forecasting#
This example uses hourly OMNI2 data from 2010–2015 to predict Dst one hour ahead. Each input contains the preceding three hourly values of solar-wind speed, GSM (B_z), average magnetic-field magnitude, and Dst.
Windows are created separately inside the training years (2010–2013), validation year (2014), and final test year (2015). Scaling is fit using only the training data. The neural model retains the source architecture:
12 inputs → 50 ReLU → 30 ReLU → 1 linear output
The single reference prediction is persistence. For the one-hour forecast, it assumes that the most recently observed Dst will persist:
\[
\widehat{Dst}(t+1)=Dst(t).
\]
The notebooks report MAE, RMSE, (R^2), persistence skill, time traces, and residual diagnostics for the same 2015 samples.