SWAN-SF-V2: A Multivariate Time Series Dataset for Space Weather Data Analytics; Now with AIA and Weighted SHARP Parameters
Abstract
Since its original publication in 2020, the SWAN-SF dataset has been cited in over 300 research publications. Here we describe a comprehensive update to the original multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in the Spaceweather HMI Active Region Patch (SHARP) series. As was performed in the original release, our dataset includes a cross-checked NOAA solar flare catalog that facilitates the comparative evaluation of methods designed for solar flare prediction. We discuss the methods used for data collection, cleaning, and pre-processing of the solar active region and flare data, which includes improvements to the data integration and sampling methodology. Additional parameters have been derived from the Solar Dynamics Observatory's Atmospheric Imaging Assembly to augment the original features derived from magnetogram data. Our dataset expands the period of observations to cover 7,730 MVTS data collections from active regions occurring between May 2010 and December 2025, each of which is composed of 182 flare-predictive parameters. We continue to integrate flare reports, with over 39,000 multi-source verified flare reports providing flaring history information about the source active regions. The primary tasks enabled by the disseminated dataset continue to include optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits.
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BibTeX
@misc{kempton2026swansfv2,
title = {{SWAN-SF-V2: A Multivariate Time Series Dataset for Space Weather Data Analytics; Now with AIA and Weighted SHARP Parameters}},
author = {Dustin Kempton and Griffin Goodwin and Reet Gupta and Viacheslav M Sadykov and Rafal A. Angryk and Berkay Aydin and Manolis K. Georgoulis},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.04591},
eprint = {2610.04591},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.04591},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - SWAN-SF-V2: A Multivariate Time Series Dataset for Space Weather Data Analytics; Now with AIA and Weighted SHARP Parameters
AU - Dustin Kempton
AU - Griffin Goodwin
AU - Reet Gupta
AU - Viacheslav M Sadykov
AU - Rafal A. Angryk
AU - Berkay Aydin
AU - Manolis K. Georgoulis
PY - 2026
DA - 2026/10/03
PB - arXiv
DO - 10.48550/arxiv.2610.04591
UR - https://arxiv.org/abs/2610.04591
AB - Since its original publication in 2020, the SWAN-SF dataset has been cited in over 300 research publications. Here we describe a comprehensive update to the original multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in the Spaceweather HMI Active Region Patch (SHARP) series. As was performed in the original release, our dataset includes a cross-checked NOAA solar flare catalog that facilitates the comparative evaluation of methods designed for solar flare prediction. We discuss the methods used for data collection, cleaning, and pre-processing of the solar active region and flare data, which includes improvements to the data integration and sampling methodology. Additional parameters have been derived from the Solar Dynamics Observatory's Atmospheric Imaging Assembly to augment the original features derived from magnetogram data. Our dataset expands the period of observations to cover 7,730 MVTS data collections from active regions occurring between May 2010 and December 2025, each of which is composed of 182 flare-predictive parameters. We continue to integrate flare reports, with over 39,000 multi-source verified flare reports providing flaring history information about the source active regions. The primary tasks enabled by the disseminated dataset continue to include optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits.
ER -