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Abstract Magnetic polarity inversion lines (PILs) detected in solar active regions have long been recognized as arguably the most essential feature for triggering instabilities such as flares and eruptive events (i.e., eruptive flares and coronal mass ejections). In recent years, efforts have been focused on using features engineered from PILs for solar eruption prediction. However, PIL rasters and metadata are often generated as by-products and are not accessible for public use, which limits their utilization in data-intensive space weather analytics applications. We introduce a large-scale publicly available PIL data set covering practically the entire solar cycle 24 for applying to various space weather forecasting and analytics tasks. The data set is created using both radial magnetic field (B_r) and line-of-sight (B_LoS) magnetograms from the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager Active Region Patches (HARP) that involve 4090 HARP series ranging from 2010 May to 2019 March. This data set includes three PIL-related binary masks of rasters: the actual PILs as per the spatial analysis of the magnetograms, the region of polarity inversion, and the convex hull of PILs, along with time-series-structured metadata extracted from these masks. We also provide a preliminary exploratory analysis of selected features aiming to correlate time series of feature metadata and eruptive activity originating from active regions. We envision that this comprehensive PIL data set will complement existing data sets used for space weather forecasting and benefit research in related areas, specifically in better understanding the PIL structure, evolution, and role in eruptions. 

Abstract We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either “horizontally” – by adding more prediction-specific parameters, or “vertically” – by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future. 

Several natural phenomena, such as floods, earth-quakes, volcanic eruptions, or extreme space weather events often come with severity indexes. While these indexes, whether linear or logarithmic are vital, data-driven predictive models for these events rather use a fixed threshold. In this paper, we explore encoding this ordinality to enhance the performance of data-driven models, with specific application in solar flare forecasting. The prediction of solar flares is commonly approached as a binary forecasting problem, categorizing events as either Flare (FL) or No-Flare (NF) based on a chosen threshold (e.g., >C-class, > M-class, or >X-class). However, this binary formulation overlooks the inherent ordinality between the sub-classes within each binary class (FL and NF). In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cross-entropy (BCE) loss function. This modification is intended to provide the model with better guidance based on the ordinal characteristics of the data and improve the overall performance of the models. For our experiments, we employ a ResNet34-based model with transfer learning to predict 2:M-class flares by utilizing the shape-based features of magnetograms of active region (AR) patches spanning from -90° to +90°of solar longitude as our input data. We use a composite skill score (CSS) as our evaluation metric, which is calculated as the geometric mean of the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare our models' performance. The primary contributions of this work are as follows: (i) We introduce a novel approach to encode ordinality into a binary loss function showing an application to solar flare prediction, (ii) We enhance solar flare forecasting by enabling flare predictions for each AR across the entire solar disk, without any longitudinal restrictions, and evaluate and compare performance. (iii) Our candidate model, optimized with the proposed loss function, shows an improvement of (~17%, (~14%, and (~13% for AR patches within ±30°, ±60°, and ±90° of solar longitude, respectively in terms of CSS, when compared with standard BCE. Additionally, we demonstrate the ability to issue flare forecasts for ARs in near-limb regions (regions between ±60° to ±90°) with a CSS=0.34 (TSS=0.50 and HSS=0.23), expanding the scope of AR-based models for solar flare prediction. This advances the reliability of solar flare forecasts, leading to more effective prediction capabilities. 

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015) , we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the plethora of methods is typically far from perfect, with physical reasons to blame, besides practical shortcomings: imperfect data, data gaps and a lack of multiple, and meaningful, vantage points of solar observations. We briefly discuss these issues, too, that shape our desired short- and long-term objectives for an efficient future predictive capability. A central aim of this article is to trigger meaningful, targeted discussions that will compel the community to adopt standards for performance verification and validation, which could be maintained and enriched by institutions such as NASA’s Community Coordinated Modeling Center (CCMC) and the community-driven COSPAR/ISWAT initiative. 

This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of ≥M1.0-class flares within 24 h. We leveraged custom data augmentation and sample weighting to counter the inherent class-imbalance problem and used true skill statistic and Heidke skill score as evaluation metrics. Recent advancements in gradient-based attention methods allow us to interpret models by sending gradient signals to assign the burden of the decision on the input features. We interpret our model using three post hoc attention methods: (i) Guided Gradient-weighted Class Activation Mapping, (ii) Deep Shapley Additive Explanations, and (iii) Integrated Gradients. Our analysis shows that full-disk predictions of solar flares align with characteristics related to the active regions. The key findings of this study are: (1) We demonstrate that our full disk model can tangibly locate and predict near-limb solar flares, which is a critical feature for operational flare forecasting, (2) Our candidate model achieves an average TSS=0.51±0.05 and HSS=0.38±0.08, and (3) Our evaluation suggests that these models can learn conspicuous features corresponding to active regions from full-disk magnetograms. 

In this work, we investigate the impact of class imbalance on the accuracy and diversity of synthetic samples generated by conditional generative adversarial networks (CGAN) models. Though many studies utilizing GANs have seen extraordinary success in producing realistic image samples, these studies generally assume the use of well-processed and balanced benchmark image datasets, including MNIST and CIFAR-10. However, well-balanced data is uncommon in real world applications such as detecting fraud, diagnosing diabetes, and predicting solar flares. It is well known that when class labels are not distributed uniformly, the predictive ability of classification algorithms suffers significantly, a phenomenon known as the "class-imbalance problem." We show that the imbalance in the training set can also impact sample generation of CGAN models. We utilize the well known MNIST datasets, controlling the imbalance ratio of certain classes within the data through sampling. We are able to show that both the quality and diversity of generated samples suffer in the presence of class imbalances and propose a novel framework named Two-stage CGAN to produce high-quality synthetic samples in such cases. Our results indicate that the proposed framework provides a significant improvement over typical oversampling and undersampling techniques utilized for class imbalance remediation. 

Quantifying the similarity or distance between time series, processes, signals, and trajectories is a task-specific problem and remains a challenge for many applications. The simplest measure, meaning the Euclidean distance, is often dismissed because of its sensitivity to noise and the curse of dimensionality. Therefore, elastic mappings (such as DTW, LCSS, ED) are often utilized instead. However, these measures are not metric functions, and more importantly, they must deal with the challenges intrinsic to point-to-point mappings, such as pathological alignment. In this paper, we adopt an object-similarity measure, namely Multiscale Intersection over Union (MIoU), for measuring the distance/similarity between time series. We call the new measure TS-MIoU. Unlike the most popular time series similarity measures, TS-MIoU does not rely on a point-to-point mapping, and therefore, circumvents all respective challenges. We show that TS-MIoU is indeed a metric function, especially that it holds the triangle inequality axiom, and therefore can take advantage of indexing algorithms without a lower bounding. We further show that its sensitivity to noise is adjustable, which makes it a strong alternative to the Euclidean distance while not suffering from the curse of dimensionality. Our proof-of-concept experiments on over 100 UCR datasets show that TS-MIoU can fill the gap between the unforgiving strictness of the ℓp-norm measures, and the mapping challenges of elastic measures.