Defects and Inconsistencies in Solar Flare Data Sources: Implications for Machine Learning Forecasting

TL;DR

This paper investigates defects and inconsistencies in solar flare data sources, demonstrating their impact on machine learning forecast accuracy and proposing procedures to mitigate these issues for improved operational predictions.

Contribution

It identifies and quantifies data defects in solar flare datasets, evaluates their effects on ML forecasting models, and offers mitigation procedures and recommendations for better data usage.

Findings

Data defects significantly affect forecast accuracy.

Mitigation procedures improve model performance.

Recommendations enhance operational forecasting reliability.

Abstract

Machine learning models for forecasting solar flares have been trained and evaluated using a variety of data sources, including Space Weather Prediction Center (SWPC) operational and science-quality data. Typically, data from these sources is minimally processed before being used to train and validate a forecasting model. However, predictive performance can be affected if defects and inconsistencies between these data sources are ignored. For a set of commonly used data sources, along with the software that queries and outputs processed data, we identify their defects and inconsistencies, quantify their extent, and show how they can affect predictions from data-driven machine-learning forecasting models. We also outline procedures for fixing these issues or at least mitigating their impacts. Finally, based on thorough comparisons of the effects of data sources on the trained forecasting model's predictive skill scores, we offer recommendations for using different data products in operational forecasting.