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Abstract We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem. Taking full advantage of pre-flare time series in solar active regions is made possible via the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, a partitioned collection of multivariate time series of active region properties comprising 4075 regions and spanning over 9 yr of the Solar Dynamics Observatory period of operations. We showcase the general concept of temporal coherence triggered by the demand of continuity in time series forecasting and show that lack of proper understanding of this effect may spuriously enhance models’ performance. We further address another well-known challenge in rare-event prediction, namely, the class-imbalance issue. The SWAN-SF is an appropriate data set for this, with a 60:1 imbalance ratio for GOES M- and X-class flares and an 800:1 imbalance ratio for X-class flares against flare-quiet instances. We revisit the main remedies for these challenges and present several experiments to illustrate the exact impact that each of these remedies may have on performance. Moreover, we acknowledge that some basic data manipulation tasks such as data normalization and cross validation may also impact the performance; we discuss these problems as well. In this framework we also review the primary advantages and disadvantages of using true skill statistic and Heidke skill score, two widely used performance verification metrics for the flare-forecasting task. In conclusion, we show and advocate for the benefits of time series versus point-in-time forecasting, provided that the above challenges are measurably and quantitatively addressed.
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This content will become publicly available on September 16, 2026
Evaluating Time-series Augmentation Techniques for Deep Learning–Based Solar Flare Prediction
Abstract Accurate forecasting of solar flares is crucial for mitigating their severe impacts on space-based and communication systems. Deep learning models have shown promise in predicting flare activity using magnetic field measurements of solar active regions. However, the scarcity and imbalance of flare occurrence data—particularly for major flares—pose significant challenges to model robustness and generalization. This study provides a comprehensive evaluation of time-series data augmentation techniques to improve deep learning-based solar flare prediction. Using the benchmark Space Weather Analytics for Solar Flares data set, which offers multivariate magnetic field parameter time series, we assess 12 augmentation methods across three architectures: fully convolutional network (FCN), multivariate LSTM-FCN, and residual network. Our primary experiments address the binary classification of major (M- and X-class) versus minor (C-, B-, and FQ-class) flares, using metrics tailored for imbalanced data, including recall, true skill statistic, Heidke skill score, and the Gini coefficient. A case study on X versus M classification further examines performance under a simpler, more balanced setting. Results show that select augmentation strategies yield measurable gains across different models and scenarios, offering a viable path forward for addressing data scarcity in space weather forecasting tasks.
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- PAR ID:
- 10673915
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- The Astrophysical Journal Supplement Series
- Volume:
- 280
- Issue:
- 2
- ISSN:
- 0067-0049
- Page Range / eLocation ID:
- 52
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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