  In this work, we develop a comprehensive framework for F10.7, S10.7, M10.7,
and Y10.7 solar driver forecasting with a time series Transformer (PatchTST).
To ensure an equal representation of high and low levels of solar activity, we
construct a custom loss function to weight samples based on the distance
between the solar driver's historical distribution and the training set. The
solar driver forecasting framework includes an 18-day lookback window and
forecasts 6 days into the future. When benchmarked against the Space
Environment Technologies (SET) dataset, our model consistently produces
forecasts with a lower standard mean error in nearly all cases, with improved
prediction accuracy during periods of high solar activity. All the code is
available on Github https://github.com/ARCLab-MIT/sw-driver-forecaster.
