Does Imaging of Time Series Help Flare Forecasting?
Channel: COSPAR2021 Workshops
Date: 02/02/2021

Authors: Yang Chen, Anli Ji, Pavan Ajit Babajiyavar, Azim Ahmadzadeh, Rafal Angryk

Abstract: Forecasting the occurrence of solar flares is one of the essential components in the space weather study. Many researchers have investigated various machine learning techniques to classify strong and weak solar flares during the past two decades. The development of the solar flare forecasting benchmark dataset based on the pre-flare time series of solar magnetic field parameters, named SWAN-SF, has provided an opportunity for taking advantage of multivariate time series (MVTS) to achieve higher performance and enhance the robustness of the new forecasting models. In this research, we approach the flare forecasting problem from a new angle. We encode MVTS data as multi-channel image data using imaging algorithms, Gramian Angular Fields (GAF) and Markov Transition Fields (MTF), to explore the proven strength of deep neural networks in the Image Processing domain. Motivated by GAF, we put forward a brand new imaging matrix, and our experiments show that it significantly improves the performance of 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 flares classification, and the other, to train a Convolutional Neural Network (CNN) on the derived images using GAF, MTF, and our modified GAF.