Comparative Study of Machine Learning Models on Solar Flare Prediction Problem

Solar flare events are explosions of energy and radiation from the Sun’s surface. These events occur due to the tangling and twisting of magnetic fields associated with sunspots. When Coronal Mass ejections accompany solar flares, solar storms could travel towards earth at very high speeds, disrupting all earthly technologies and posing radiation hazards to astronauts. For this reason, the prediction of solar flares has become a crucial aspect of forecasting space weather. Our thesis utilized the time-series data consisting of active solar region magnetic field parameters acquired from SDO that span more than eight years. The classification models take AR data from an observation period of 12 hours as input to predict the occurrence of flare in next 24 hours. We performed preprocessing and feature selection to find optimal feature space consisting of 28 active region parameters that made our multivariate time series dataset (MVTS). For the first time, we modeled the flare prediction task as a 4-class problem and explored a comprehensive set of machine learning models to identify the most suitable model. This research achieved a state-of-the-art true skill statistic (TSS) of 0.92 with a 99.9% recall of X-/M- class flares on our time series forest model. This was accomplished with the augmented dataset in which the minority class is over-sampled using synthetic samples generated by SMOTE and the majority classes are randomly under-sampled. This work has established a robust dataset and baseline models for future studies in this task, including experiments on remedies to tackle the class imbalance problem such as weighted cost functions and data augmentation. Also the time series classifiers implemented will enable shapelets mining that can provide interpreting ability to domain experts

Augmented Tensor Decomposition with Stochastic Optimization

Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals. Existing tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting raw data under statistical assumptions, which may not align with downstream classification tasks. Also, real-world tensor data are usually high-ordered and have large dimensions with millions or billions of entries. Thus, it is expensive to decompose the whole tensor with traditional algorithms. In practice, raw tensor data also contains redundant information while data augmentation techniques may be used to smooth out noise in samples. This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations to boost downstream classification. To reduce the memory footprint of the decomposition, we propose a stochastic algorithm that updates the factor matrices in a batch fashion. We evaluate ATD on multiple signal datasets. It shows comparable or better performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder baselines with less than 5% of model parameters, achieves 0.6% ~ 1.3% accuracy gain over other tensor-based baselines, and reduces the memory footprint by 9X when compared to standard tensor decomposition algorithms.Comment: Fixed some typo