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Abstract The accurate prediction of solar flares is crucial due to their risks to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by employing sophisticated data preprocessing and sampling techniques for the Space Weather Analytics for Solar Flares (SWAN-SF) data set, a rich source of multivariate time series data of solar active regions. Our study adopts a multifaceted approach encompassing four key methodologies. Initially, we address over 10 million missing values in the SWAN-SF data set through our innovative imputation technique called fast Pearson correlation-based k-nearest neighbors imputation. Subsequently, we propose a precise normalization technique, called LSBZM normalization, tailored for time series data, merging various strategies (log, square root, Box–Cox, Z-score, and min–max) to uniformly scale the data set's 24 attributes (photospheric magnetic field parameters), addressing issues such as skewness. We also explore the “near decision boundary sample removal” technique to enhance the classification performance of the data set by effectively resolving the challenge of class overlap. Finally, a pivotal aspect of our research is a thorough evaluation of diverse oversampling and undersampling methods, including SMOTE, ADASYN, Gaussian noise injection, TimeGAN, Tomek links, and random undersampling, to counter the severe imbalance in the SWAN-SF data set, notably a 60:1 ratio of major (X and M) to minor (C, B, and FQ) flaring events in binary classification. To demonstrate the effectiveness of our methods, we use eight classification algorithms, including advanced deep-learning-based architectures. Our analysis shows significant true skill statistic scores, underscoring the importance of data preprocessing and sampling in time-series-based solar flare prediction. 

Abstract Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes. 

Abstract Solar energetic particles (SEPs) are associated with extreme solar events that can cause major damage to space- and ground-based life and infrastructure. High-intensity SEP events, particularly ∼100 MeV SEP events, can pose severe health risks for astronauts owing to radiation exposure and affect Earth’s orbiting satellites (e.g., Landsat and the International Space Station). A major challenge in the SEP event prediction task is the lack of adequate SEP data because of the rarity of these events. In this work, we aim to improve the prediction of ∼30, ∼60, and ∼100 MeV SEP events by synthetically increasing the number of SEP samples. We explore the use of a univariate and multivariate time series of proton flux data as input to machine-learning-based prediction methods, such as time series forest (TSF). Our study covers solar cycles 22, 23, and 24. Our findings show that using data augmentation methods, such as the synthetic minority oversampling technique, remarkably increases the accuracy and F1-score of the classifiers used in this research, especially for TSF, where the average accuracy increased by 20%, reaching around 90% accuracy in the ∼100 MeV SEP prediction task. We also achieved higher prediction accuracy when using the multivariate time series data of the proton flux. Finally, we build a pipeline framework for our best-performing model, TSF, and provide a comprehensive hierarchical classification of the ∼100, ∼60, and ∼30 MeV and non-SEP prediction scenarios. 

The purpose of this study is to provide a comprehensive resource for the selection of data representations for machine learning-oriented models and components in solar flare prediction tasks. Major solar flares occurring in the solar corona and heliosphere can bring potential destructive consequences, posing significant risks to astronauts, space stations, electronics, communication systems, and numerous technological infrastructures. For this reason, the accurate detection of major flares is essential for mitigating these hazards and ensuring the safety of our technology-dependent society. In response, leveraging machine learning techniques for predicting solar flares has emerged as a significant application within the realm of data science, relying on sensor data collected from solar active region photospheric magnetic fields by space- and ground-based observatories. In this research, three distinct solar flare prediction strategies utilizing the photospheric magnetic field parameter-based multivariate time series dataset are evaluated, with a focus on data representation techniques. Specifically, we examine vector-based, time series-based, and graph-based approaches to identify the most effective data representation for capturing key characteristics of the dataset. The vector-based approach condenses multivariate time series into a compressed vector form, the time series representation leverages temporal patterns, and the graph-based method models interdependencies between magnetic field parameters. The results demonstrate that the vector representation approach exhibits exceptional robustness in predicting solar flares, consistently yielding strong and reliable classification outcomes by effectively encapsulating the intricate relationships within photospheric magnetic field data when coupled with appropriate downstream machine learning classifiers.