Planetary statistics and forecasting for solar flares

TL;DR

This paper explores the potential link between planetary motions and solar flare occurrences, using four decades of data and machine learning to improve forecasting accuracy.

Contribution

It introduces evidence of planetary influence on solar flares and demonstrates the application of machine learning for improved flare prediction.

Findings

Significant correlation between planetary positions and solar flares.

Data shows non-random patterns in solar flare occurrences.

Machine learning enhances flare forecasting accuracy.

Abstract

Indications are presented for a significant connection between the relative motion of the planets and the appearance of energetic solar flares. Based on the records of the last four decades, the analysis highlights remarkable features and a lack of randomness in the data. The indications are supported further by the predictive power of a preliminary application to forecasting with machine learning methods.