Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF

TL;DR

This paper introduces an improved hybrid feature selection and classification method, Slim-TSF, for solar flare forecasting, demonstrating enhanced predictive accuracy by capturing the evolving nature of solar activity.

Contribution

The study presents an updated Slim-TSF framework with systematic feature selection, significantly improving solar flare prediction accuracy over previous models.

Findings

5% increase in True Skill Statistic (TSS)

5% increase in Heidke Skill Score (HSS)

Enhanced ability to capture solar flare evolution

Abstract

Traditional solar flare forecasting approaches have mostly relied on physics-based or data-driven models using solar magnetograms, treating flare predictions as a point-in-time classification problem. This approach has limitations, particularly in capturing the evolving nature of solar activity. Recognizing the limitations of traditional flare forecasting approaches, our research aims to uncover hidden relationships and the evolutionary characteristics of solar flares and their source regions. Our previously proposed Sliding Window Multivariate Time Series Forest (Slim-TSF) has shown the feasibility of usage applied on multivariate time series data. A significant aspect of this study is the comparative analysis of our updated Slim-TSF framework against the original model outcomes. Preliminary findings indicate a notable improvement, with an average increase of 5\% in both the True Skill Statistic (TSS) and Heidke Skill Score (HSS). This enhancement not only underscores the effectiveness of our refined methodology but also suggests that our systematic evaluation and feature selection approach can significantly advance the predictive accuracy of solar flare forecasting models.