On The Effectiveness of Imaging of Time Series for Flare Forecasting Problem

Forecasting the occurrence of solar flares is a typical 21st century rare-event classification task. Over the past two decades, many studies have implemented various techniques and approaches for classification of strong and weak solar flares. The release of the recent flare forecasting benchmark dataset, named SWAN-SF, has opened the door for taking advantage of multivariate time series (MVTS) of pre-flare magnetic fields’ activity in order to potentially achieve higher performance and increase the robustness of the new forecasting models. In this study, we take a new approach and explore the effectiveness of imaging algorithms on the time series. We convert MVTS data into multi-channel image data using Gramian Angular Fields (GAF) and Markov Transition Fields (MTF) to explore the proven strength of deep neural networks in the Image Processing domain, on flares’ MVTS data. Inspired by GAF, we propose another imaging matrix that our experiments show that it significantly improves the performance of the CNN, by 120% in terms of TSS and 440% in terms of HSS2. Finally, we juxtapose two approaches to tackle the flare forecasting problem: one, to utilize a time-series specific Support Vector Classifier for classification of flares, and the other, to train a Convolutional Neural Network (CNN) on the derived images using GAF, MTF, and our modified GAF.