Data-Driven Approaches to Space Weather Prediction Problems

We present novel statistical methods for early forecasting of solar flare events and compare them with machine learning approaches we adopted in our previous work. The data sources that we use include Geostationary Operational Environmental Satellites (GOES), Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), and SDO/Atmospheric Imaging Assembly (AIA). The results that I will show in the talk include (1) strong and weak flare classification with spatial statistics features, together with physics and topological parameters; (2) active region-based solar flare intensity prediction with a mixed Long-Short Term Memory (LSTM) regression; and (3) Tensor Gaussian Process with Contraction model for solar flare forecasting combining data of various types and sources (SHARP parameters, HMI and AIA images).