Enhancing Solar Driver Forecasting with Multivariate Transformers
<|reference_start|>Enhancing Solar Driver Forecasting with Multivariate Transformers: 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.<|reference_end|>
@article{sanchez-hurtado2024enhancing,
    title={Enhancing Solar Driver Forecasting with Multivariate Transformers},
    author={Sergio Sanchez-Hurtado, Victor Rodriguez-Fernandez, Julia Briden, Peng
  Mun Siew, Richard Linares},
    journal={arXiv preprint arXiv:2406.15847},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15847},
    primaryClass={physics.space-ph cs.AI cs.LG}
}