Version 1
: Received: 4 March 2024 / Approved: 5 March 2024 / Online: 5 March 2024 (10:55:34 CET)
How to cite:
Saini, K.; Alshammari, K.; Hamdi, S.M.; Filali Boubrahimi, S. Solar Flare Prediction from Extremely Imbalanced Multivariate Time Series Data using Minimally Random Convolutional Kernel Transform. Preprints2024, 2024030210. https://doi.org/10.20944/preprints202403.0210.v1
Saini, K.; Alshammari, K.; Hamdi, S.M.; Filali Boubrahimi, S. Solar Flare Prediction from Extremely Imbalanced Multivariate Time Series Data using Minimally Random Convolutional Kernel Transform. Preprints 2024, 2024030210. https://doi.org/10.20944/preprints202403.0210.v1
Saini, K.; Alshammari, K.; Hamdi, S.M.; Filali Boubrahimi, S. Solar Flare Prediction from Extremely Imbalanced Multivariate Time Series Data using Minimally Random Convolutional Kernel Transform. Preprints2024, 2024030210. https://doi.org/10.20944/preprints202403.0210.v1
APA Style
Saini, K., Alshammari, K., Hamdi, S.M., & Filali Boubrahimi, S. (2024). Solar Flare Prediction from Extremely Imbalanced Multivariate Time Series Data using Minimally Random Convolutional Kernel Transform. Preprints. https://doi.org/10.20944/preprints202403.0210.v1
Chicago/Turabian Style
Saini, K., Shah Muhammad Hamdi and Soukaina Filali Boubrahimi. 2024 "Solar Flare Prediction from Extremely Imbalanced Multivariate Time Series Data using Minimally Random Convolutional Kernel Transform" Preprints. https://doi.org/10.20944/preprints202403.0210.v1
Abstract
Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and caused by the changes in magnetic field states in solar active regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares ranging from electronic communication disruption to radiation exposure-based health risks to the astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MINIROCKET), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations SEQuence Learner (Mr-SEQL), and Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the on the Space Weather ANalytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over 9 years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the true skill statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MINIROCKET, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics.
Keywords
Multivariate Time Series; Solar Flare; Space Weather; Imbalanced Data
Subject
Physical Sciences, Space Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
