Enhancing Solar Driver Forecasting with Multivariate Transformers

Sergio Sanchez-Hurtado, Victor Rodriguez-Fernandez, Julia Briden, Peng Mun Siew, Richard Linares
6/22/2024
physics.space-phcs.AIcs.LG

No official code found. Best implementation: ARCLab-MIT/SW-Transformers-Forecaster-Framework (3 stars, Apache-2.0, last commit 2025-07-12).

Abstract

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.

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Code Implementations(1)

ARCLab-MIT/SW-Transformers-Forecaster-FrameworkCommunity100%
Apache-2.0

Forecasting Space Weather Drivers

31CSS, PythonOct 3, 20231 years agoApache-2.0

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Cite this paper

@article{sanchezhurtado2024enhancing,
  title  = {Enhancing Solar Driver Forecasting with Multivariate Transformers},
  author = {Sergio Sanchez-Hurtado and Victor Rodriguez-Fernandez and Julia Briden and Peng Mun Siew and Richard Linares},
  year   = {2024},
  eprint = {2406.15847},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.15847v2}
}

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