Improving Solar Flare Prediction by Time Series Outlier Detection

TL;DR

This paper demonstrates that detecting and removing outliers from solar flare time series data significantly improves the accuracy and reliability of machine learning-based flare prediction models.

Contribution

The study introduces a method to identify and eliminate outliers in solar flare datasets, significantly enhancing model performance and reliability.

Findings

279% increase in True Skill Statistic

68% increase in Heidke Skill Score

Outlier removal improves flare prediction accuracy

Abstract

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.