Enhancing Solar Driver Forecasting with Multivariate Transformers

Autor:	Sanchez-Hurtado, Sergio, Rodriguez-Fernandez, Victor, Briden, Julia, Siew, Peng Mun, Linares, Richard
Rok vydání:	2024
Předmět:	Physics - Space Physics Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Short paper accepted for oral presentation at the SPAICE Conference 2024 (https://spaice.esa.int/)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.15847 Zobrazit plný text záznamu View this record from Arxiv