# How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events

arXiv: https://arxiv.org/abs/2103.07542 · HTML: https://mlsanity.com/paper/2103.07542/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 60 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [A hybrid supervised/unsupervised machine learning approach to solar flare prediction](https://mlsanity.com/paper/1706.07103/) (2017)
- [A Framework for Designing and Evaluating Solar Flare Forecasting Systems](https://mlsanity.com/paper/2005.02493/) (2020)

## Built on by (most cited first, 26 of 26)

- [Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting](https://mlsanity.com/paper/2209.07406/) (2022)
- [Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data](https://mlsanity.com/paper/2204.03710/) (2022)
- [Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods](https://mlsanity.com/paper/2307.15878/) (2023)
- [Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting](https://mlsanity.com/paper/2308.02682/) (2023)
- [Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks](https://mlsanity.com/paper/2309.04558/) (2023)
- [Surya: Foundation Model for Heliophysics](https://mlsanity.com/paper/2508.14112/) (2025)
- [Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting](https://mlsanity.com/paper/2408.11768/) (2024)
- [Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data](https://mlsanity.com/paper/2410.00312/) (2024)
- [Improving Solar Flare Prediction by Time Series Outlier Detection](https://mlsanity.com/paper/2206.07197/) (2022)
- [Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning](https://mlsanity.com/paper/2409.14016/) (2024)
- [Towards Synthetic Multivariate Time Series Generation for Flare Forecasting](https://mlsanity.com/paper/2105.07532/) (2021)
- [Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 Methods](https://mlsanity.com/paper/2109.14770/) (2021)
- [ChronoGAN: Supervised and Embedded Generative Adversarial Networks for Time Series Generation](https://mlsanity.com/paper/2409.14013/) (2024)
- [EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction](https://mlsanity.com/paper/2411.11249/) (2024)
- [VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification](https://mlsanity.com/paper/2412.16515/) (2024)
- [Using Multiple Instance Learning for Explainable Solar Flare Prediction](https://mlsanity.com/paper/2203.13896/) (2022)
- [Global Cross-Time Attention Fusion for Enhanced Solar Flare Prediction from Multivariate Time Series](https://mlsanity.com/paper/2511.12955/) (2025)
- [Construction Cost Index Forecasting: A Multi-feature Fusion Approach](https://mlsanity.com/paper/2108.10155/) (2021)
- [Probabilistic solar flare forecasting using historical magnetogram data](https://mlsanity.com/paper/2308.15410/) (2023)
- [Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction](https://mlsanity.com/paper/2405.20590/) (2024)
- [Ordinal Encoding as a Regularizer in Binary Loss for Solar Flare Prediction](https://mlsanity.com/paper/2510.04063/) (2025)
- [Beyond Point Matching: Evaluating Multiscale Dubuc Distance for Time Series Similarity](https://mlsanity.com/paper/2510.21824/) (2025)
- [Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series](https://mlsanity.com/paper/2511.01873/) (2025)
- [Enhancing Explainability in Solar Energetic Particle Event Prediction: A Global Feature Mapping Approach](https://mlsanity.com/paper/2511.09475/) (2025)
- [Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records](https://mlsanity.com/paper/2511.22958/) (2025)
- [SolarGPT-QA: A Domain-Adaptive Large Language Model for Educational Question Answering in Space Weather and Heliophysics](https://mlsanity.com/paper/2601.12131/) (2026)

## Abstract (arXiv)

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 dataset; a partitioned collection of multivariate time series of active region properties comprising 4075 regions and spanning over 9 years of the Solar Dynamics Observatory (SDO) 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 dataset for this, with a 60:1 imbalance ratio for GOES M- and X-class flares and a 800:1 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, as two widely used performance verification metrics for the flare forecasting task. In conclusion, we show and advocate for the benefits of time series vs. point-in-time forecasting, provided that the above challenges are measurably and quantitatively addressed.