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Multivariate time series dataset for space weather data analytics.

Rafal A Angryk1, Petrus C Martens2, Berkay Aydin3

  • 1Department of Computer Science, Georgia State University, Atlanta, United States. rangryk@gsu.edu.

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This summary is machine-generated.

We released a new solar flare dataset from photospheric magnetograms, aiding prediction research. This comprehensive multivariate time series (MVTS) data and flare catalog offer valuable insights for space weather forecasting.

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Area of Science:

  • Solar Physics
  • Space Weather Research
  • Data Science

Background:

  • Solar flares pose risks to space technology.
  • Accurate prediction of solar flares is crucial for mitigating these risks.
  • Existing datasets may lack the comprehensive nature required for advanced predictive modeling.

Purpose of the Study:

  • To introduce and provide open access to a new multivariate time series (MVTS) dataset.
  • To facilitate solar flare prediction efforts through a comprehensive dataset and integrated flare catalog.
  • To enable research into flare predictors and precursors.

Main Methods:

  • Extraction of MVTS data from Spaceweather HMI Active Region Patch (SHARP) series magnetograms.
  • Integration with a cross-checked NOAA solar flare catalog.
  • Development of novel data integration and sampling methodologies for solar active region and flare data.

Main Results:

  • A dataset covering 4,098 MVTS collections from May 2010 to December 2018.
  • Inclusion of 51 flare-predictive parameters and over 10,000 integrated flare reports.
  • Open accessibility of the comprehensive dataset for research.

Conclusions:

  • The dataset supports optimization of solar flare prediction models.
  • Facilitates detailed investigation into flare predictors and precursors.
  • Offers potential for future expansion into integrated solar eruption prediction.