Over the past few decades, the synergy of physics-based feature engineering and data-intensive methods, including machine learning and deep learning, has ushered in a new era in the analysis and prediction of space weather forecasting, specifically for solar flare prediction. These sophisticated approaches play a pivotal role in understanding the complex mechanisms leading to solar flares, with a primary focus on forecasting these events and mitigating potential risks they pose to our planet. While current methodologies have made substantial advancements, they are not without limitations, and one particularly glaring limitation is the neglect of temporal evolution characteristics within the active regions from which solar flares originate. This oversight impairs the capacity of these methods to capture the intricate relationships among high-dimensional features of these active regions, thereby constraining their practical utility. Our study focuses on two key objectives: the development of interpretable classifiers for multivariate time series data and the introduction of an innovative feature ranking method using sliding window-based sub-interval ranking. 

Solar energetic particle (SEP) events, as one of the most dangerous manifestations of solar activity, can generate severe hazardous radiation when accelerated by solar flares or shock waves formed aside coronal mass ejections (CMEs). Unlike common predictions that focus on the occurrence of an event, an All-Clear forecast puts more emphasis on predicting the absence of an event. Such forecasts, while usually not addressed directly, can be crucial in operational environments. We have developed an All-Clear SEP event prediction system utilizing active region-based prediction methods together with active region scenarios (i.e., location and complexity). Within our All-Clear forecast system, signals are generated only when requested as binary predictions of YES or NO indicating “All Clear” or “Not All Clear”, respectively.  

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An all-clear flare prediction is a type of solar flare forecasting that puts more emphasis on predicting non-flaring instances (often relatively small flares and flare quiet regions) with high precision while still maintaining valuable predictive results. While many flare prediction studies do not address this problem directly, all-clear predictions can be useful in operational context. However, in all-clear predictions, finding the right balance between avoiding false negatives (misses) and reducing the false positives (false alarms) is often challenging. Our study focuses on training and testing a set of interval-based time series named Time Series Forest (TSF). These classifiers will be used towards building an all-clear flare prediction system by utilizing multivariate time series data. Throughout this paper, we demonstrate our data collection, predictive model building and evaluation processes, and compare our time series classification models with baselines using our benchmark datasets.

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we present a deep-learning based model for full-disk solar flare prediction using compressed magnetogram images to perform operations-ready flare forecasts. We selected two prediction modes, which are both binary for predicting the occurrence of ≥M1.0 and ≥C1.0 class flares within the next 24 hours. We followed two time- segmented cross-validation strategies: chronological and non-chronological, to effectively understand the predictive skill of our models. Our candidate model achieves an average TSS of 0.47±0.06 for ≥M1.0 mode and 0.63±0.05 for ≥C1.0 mode, and HSS of 0.35±0.05 for ≥M1.0 and 0.62±0.05 for ≥C1.0 mode. Our experimental evaluation suggests that training a flare prediction model is heavily influenced by the sampling strategies involved due to the imbalanced nature of the datasets and predicting ≥M1.0 class flares is a more challenging task compared to ≥C1.0 ones.

This work progresses the growing body of research on full-disk solar flare prediction using deep learning approaches. We utilized three well-known pretrained CNN models—AlexNet, VGG16 and ResNet34 and train our models in three prediction modes, among which two are binary for predicting the occurrence of ≥M1.0 and ≥C4.0 class flares and one is a multi-class mode for predicting the occurrence of <C4.0, [≥C4.0, <M1.0] and ≥M1.0 within the next 24 hours. Our candidate model for multi-class flare prediction achieves an average TSS of 0.36 and average HSS of 0.31. Similarly, for binary prediction in (i)≥C4.0 mode: we achieve an average TSS score of 0.47 and HSS score of 0.46 (ii)≥M1.0 mode: we achieve an average TSS score of 0.55 and HSS score of 0.43.

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In the process of creating instances from time series data using a sliding window, we derive a set of new labels:

1. Maximum relative X-ray flux increase (rxfi Max)

2. Cumulative weighted GOES subclass values (GC )

3. Cumulative rxfi values (rxfi )

For rxfi Max, the maximum relative X-ray flux increase in the prediction window is used as a label. For cumulative indices, the sum of the weighted GOES subclass values or rxfi values are used as the index. We also implement a case study on an active region-based model. The Time Series Forest classifier (TSF) is trained with these three new labels. Our results indicate that new labels provide comparable skill scores to well-known techniques and thus can be considered as a new approach in solar flare forecasting. 

We have developed a RESTful API to more easily search and access information about various Magnetic Polarity Inversion Line datasets. Researchers can retrieve rasters and relevant metadata from 2010 to 2019 by searching for a specific ID, a time range, or a spatiotemporal range. The API is designed to be extensible and to incorporate other MPIL datasets, identification formats, and raster data.

We have built a large-scale dataset of the solar active region was specifically for space weather analytics for Solar Flares (SWAN-SF). The dataset contains metadata from over 4,000 trajectories of the solar active region patches, which are integrated with carefully curated solar flare data over the best part of one complete solar cycle, almost as long as SDO/HMI commissioning time. 

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This local outlier detection involves three phases. In the first phase, we apply a temporal partition procedure to divide the raw trajectory into multiple trajectory segments and extract trajectory features from spatial and temporal attributes for each trajectory segment. Then, we generate template features of trajectory segments by applying a clustering schema in the second phase. Finally, we use the abnormal score - a novel dissimilarity measure, which quantifies the disparity among the query and template trajectory segments in terms of trajectory features and hence determines the local outliers based on the distribution of abnormal score. To demonstrate the effectiveness of our method, we conduct three case studies on the real-life spatio-temporal trajectory datasets from the solar astroinformatics domain (i.e., solar active regions, coronal mass ejections, polarity inversion lines (PIL)). Our experimental results show that our local outlier detection approach can effectively discover the erroneous reports from the reporting module and abnormal phenomenon in various spatio-temporal trajectory datasets. 

 We show that through our experimental evaluation our models perform better than baselines and Sub-Pixel CNN super resolution model provides viable results for magnetogram super resolution. 

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