# Improving Solar Flare Prediction by Time Series Outlier Detection

arXiv: https://arxiv.org/abs/2206.07197 · HTML: https://mlsanity.com/paper/2206.07197/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events](https://mlsanity.com/paper/2103.07542/) (2021)
- [A hybrid supervised/unsupervised machine learning approach to solar flare prediction](https://mlsanity.com/paper/1706.07103/) (2017)
- [Towards Synthetic Multivariate Time Series Generation for Flare Forecasting](https://mlsanity.com/paper/2105.07532/) (2021)

## Built on by (most cited first, 3 of 3)

- [FLARE: A Framework for Stellar Flare Forecasting using Stellar Physical Properties and Historical Records](https://mlsanity.com/paper/2502.18218/) (2025)
- [Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction](https://mlsanity.com/paper/2405.20590/) (2024)
- [Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series](https://mlsanity.com/paper/2511.01873/) (2025)

## Abstract (arXiv)

Solar flares not only pose risks to outer space technologies and astronauts' well being, but also cause disruptions on earth to our hight-tech, interconnected infrastructure our lives highly depend on. While a number of machine-learning methods have been proposed to improve flare prediction, none of them, to the best of our knowledge, have investigated the impact of outliers on the reliability and those models' performance. In this study, we investigate the impact of outliers in a multivariate time series benchmark dataset, namely SWAN-SF, on flare prediction models, and test our hypothesis. That is, there exist outliers in SWAN-SF, removal of which enhances the performance of the prediction models on unseen datasets. We employ Isolation Forest to detect the outliers among the weaker flare instances. Several experiments are carried out using a large range of contamination rates which determine the percentage of present outliers. We asses the quality of each dataset in terms of its actual contamination using TimeSeriesSVC. In our best finding, we achieve a 279% increase in True Skill Statistic and 68% increase in Heidke Skill Score. The results show that overall a significant improvement can be achieved to flare prediction if outliers are detected and removed properly.