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

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Big Data Analytics and Knowledge Discovery (DaWaK 2024)

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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.

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Acknowledgment

This work is supported in part under two grants from NSF (Award #2104004) and NASA (SWR2O2R Grant #80NSSC22K0272).

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Authors and Affiliations

  1. Georgia State University, Atlanta, USA

    Anli Ji, Chetraj Pandey & Berkay Aydin

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  1. Anli Ji
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  2. Chetraj Pandey
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  3. Berkay Aydin
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Corresponding author

Correspondence to Anli Ji .

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Editors and Affiliations

  1. Poznan University of Technology, Poznan, Poland

    Robert Wrembel

  2. Polytechnic University of Turin, Turin, Italy

    Silvia Chiusano

  3. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Ji, A., Pandey, C., Aydin, B. (2024). Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSF. In: Wrembel, R., Chiusano, S., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2024. Lecture Notes in Computer Science, vol 14912. Springer, Cham. https://doi.org/10.1007/978-3-031-68323-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-68323-7_7

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