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Abstract Timely and accurate prediction of solar flares is a crucial task due to the danger they pose to human life and infrastructure beyond Earth’s atmosphere. Although various machine learning algorithms have been employed to improve solar flare prediction, there has been limited focus on improving performance using outlier detection. In this study, we propose the use of a tree-based outlier detection algorithm, Isolation Forest (iForest), to identify multivariate time-series instances within the flare-forecasting benchmark data set, Space Weather Analytics for Solar Flares (SWAN-SF). By removing anomalous samples from the nonflaring class (N-class) data, we observe a significant improvement in both the true skill score and the updated Heidke skill score in two separate experiments. We focus on analyzing outliers detected by iForest at a 2.4% contamination rate, considered the most effective overall. Our analysis reveals a co-occurrence between the outliers we discovered and strong flares. Additionally, we investigated the similarity between the outliers and the strong-flare data and quantified it using Kullback–Leibler divergence. This analysis demonstrates a higher similarity between our outliers and strong-flare data when compared to the similarity between the outliers and the rest of the N-class data, supporting our rationale for using outlier detection to enhance SWAN-SF data for flare prediction. Furthermore, we explore a novel approach by treating our outliers as if they belong to flaring-class data in the training phase of our machine learning, resulting in further enhancements to our models’ performance. 

Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters. In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM). We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multivariate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. The resulting classifiers’ performance after augmentation outplayed the traditional flare forecasting techniques.