EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction
Year: 2024
Type: conference-paper
Abstract: In heliophysics research, predicting solar flares is crucial due to their potential to substantially impact both space-based systems and Earth’s infrastructure. Magnetic field data from solar active regions, recorded by solar imaging observatories, are transformed into multivariate time series to enable solar flare prediction using temporal window-based analysis. In the realm of multivariate time series-driven solar flare prediction, addressing severe class imbalance with effective strategies for multivariate time series representation learning is key to developing robust predictive models. Traditional methods often struggle with overfitting to the majority class in prediction tasks where major solar flares are infrequent. This work presents EX-CON, a contrastive representation learning framework designed to enhance classification performance amidst such imbalances. EXCON operates through four stages: (1) obtaining core features from multivariate time series data; (2) selecting distinctive contrastive representations for each class to maximize inter-class separation; (3) training a temporal feature embedding module with a custom extreme reconstruction loss to minimize intra-class variation; and (4) applying a classifier to the learned embeddings for robust classification. The proposed method leverages contrastive learning principles to map similar instances closer in the feature space while distancing dissimilar ones, a strategy not extensively explored in solar flare prediction tasks. This approach not only addresses class imbalance but also offers a versatile solution applicable to both univariate and multivariate time series across binary and multiclass classification problems. Experimental results, including evaluations on the benchmark solar flare dataset and multiple time series archive datasets with binary and multiclass labels, demonstrate EXCON’s efficacy in enhancing classification performance and reducing overfitting.
Institution: Utah State University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: SHINE: Understanding the Relationships of Photospheric Vector Magnetic Field Parameters in Solar Flare Occurrences using Graph-based Machine Learning Models, CRII: OAC: Cyberinfrastructure for Machine Learning on Multivariate Time Series Data and Functional Networks, CAREER: End-to-End Active Region-based Heliospheric Forecasting System Using Multi-spacecraft Data and Machine Learning, G8101356508, Combining Physics and Machine Learning-based Models for Full-Energy-Range Solar Energetic Particles Events Prediction
OpenAlex ID: W4406459265