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\begin{document}

\title{Avoiding More Accurate and Less Robust Models: Mistakes and Pitfalls in
Training Flare Forecasting Models}



\author[1]{Azim Ahmadzadeh}%
\author[1]{Berkay Aydin}%
\author[1]{Dustin Kempton}%
\author[1]{Sushant Mahajan}%
\author[2]{Manolis Georgoulis}%
\author[1]{Rafal Angryk}%
\affil[1]{Georgia State University}%
\affil[2]{RCAAM of the Academy of Athens}%


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  \date{May 22, 2024}


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\begin{abstract}
In spite of more than 20 years of substantial advances, solar flare
prediction remains a largely outstanding problem. This is partly because
of the scarcity of major flares. Effective flare prediction, if ever
achieved, would help mitigate a substantial projected economic damage,
with a long-range magnitude of 1 to 2 trillion dollars for the US alone.
Prediction could also help mitigate, or even prevent, serious health
risks to astronauts exposed to flares' electromagnetic radiation and
particulate. While many recent flare prediction studies have opted to
employ Machine Learning techniques to better tackle the problem, a lack
of sufficient understanding of how to properly treat the data often
leads to overly optimistic results. We use the recently generated GSU
solar flare benchmark dataset, called Space Weather ANalytics for Solar
Flares (SWAN-SF), to show how a `mediocre' forecast model can turn into
an `impressive' one, by simply overlooking some basic practices in data
mining and machine learning. The benchmark is a multivariate time series
collection, extracted from magnetographic measurements in the solar
photosphere and spans over eight years of the Solar Dynamics Observatory
Helioseismic and Magnetic Imager (SDO/HMI) era. We briefly explain the
data collection process, the sampling and the slicing of time series,
and then outline a series of experiments using machine learning models
to illustrate the common mistakes, fallacies and pitfalls in forecasting
rare events. We particularly elaborate on how and why imbalanced
datasets, in general, impact the models' performance, and how different
under- or over-sampling methodologies and weighting practices could
introduce accurate but often weak models. Concluding, we aim to draw
attention to the impact of these practices on the flare forecasting
models and how to train models by accentuating the statistical
robustness over a relative accuracy in prediction.%
\end{abstract}%



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