# Predicting Solar Flares Using a Long Short-Term Memory Network

arXiv: https://arxiv.org/abs/1905.07095 · HTML: https://mlsanity.com/paper/1905.07095/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 25 of 25)

- [Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy](https://mlsanity.com/paper/2211.03796/) (2022)
- [Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data](https://mlsanity.com/paper/2204.03710/) (2022)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Tracing Halpha Fibrils through Bayesian Deep Learning](https://mlsanity.com/paper/2107.07886/) (2021)
- [Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network](https://mlsanity.com/paper/2203.14393/) (2022)
- [Operational solar flare forecasting via video-based deep learning](https://mlsanity.com/paper/2209.05128/) (2022)
- [A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data](https://mlsanity.com/paper/2211.02278/) (2022)
- [Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection](https://mlsanity.com/paper/2108.00670/) (2021)
- [Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods](https://mlsanity.com/paper/2209.13779/) (2022)
- [Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model](https://mlsanity.com/paper/2503.03237/) (2025)
- [Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network](https://mlsanity.com/paper/2403.18302/) (2024)
- [VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification](https://mlsanity.com/paper/2412.16515/) (2024)
- [Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning](https://mlsanity.com/paper/2501.01011/) (2025)
- [Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images](https://mlsanity.com/paper/2508.07847/) (2025)
- [Using Multiple Instance Learning for Explainable Solar Flare Prediction](https://mlsanity.com/paper/2203.13896/) (2022)
- [Incorporating Polar Field Data for Improved Solar Flare Prediction](https://mlsanity.com/paper/2212.01730/) (2022)
- [On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee](https://mlsanity.com/paper/2303.06815/) (2023)
- [Solar Active Region Magnetogram Image Dataset for Studies of Space Weather](https://mlsanity.com/paper/2305.09492/) (2023)
- [EVEREST: An Evidential, Tail-Aware Transformer for Rare-Event Time-Series Forecasting](https://mlsanity.com/paper/2601.19022/) (2026)
- [Ensemble Learning for CME Arrival Time Prediction](https://mlsanity.com/paper/2305.00258/) (2023)
- [Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification](https://mlsanity.com/paper/2402.17196/) (2024)
- [An Interpretable Machine Learning Approach to Understanding the Relationships between Solar Flares and Source Active Regions](https://mlsanity.com/paper/2502.15066/) (2025)
- [FLARE-SSM: Deep State Space Models with Influence-Balanced Loss for 72-Hour Solar Flare Prediction](https://mlsanity.com/paper/2509.09988/) (2025)
- [Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network](https://mlsanity.com/paper/2604.10016/) (2026)
- [DiffUNet^2: Bidirectional Conditional Diffusion for Probabilistic Scientific Spatiotemporal Modeling](https://mlsanity.com/paper/2606.03926/) (2026)

## Abstract (arXiv)

We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a gamma-class flare within the next 24 hours. We consider three gamma classes, namely >=M5.0 class, >=M class, and >=C class, and build three LSTM models separately, each corresponding to a gamma class. Each LSTM model is used to make predictions of its corresponding gamma-class flares. The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. Each data sample has 40 features including 25 magnetic parameters obtained from the Space-weather HMI Active Region Patches (SHARP) and related data products as well as 15 flare history parameters. We survey the flare events that occurred from 2010 May to 2018 May, using the GOES X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select flares with identified ARs in the NCEI flare catalogs. These flare events are used to build the labels (positive vs. negative) of the data samples. Experimental results show that (i) using only 14-22 most important features including both flare history and magnetic parameters can achieve better performance than using all the 40 features together; (ii) our LSTM network outperforms related machine learning methods in predicting the labels of the data samples. To our knowledge, this is the first time that LSTMs have been used for solar flare prediction.