We introduce and make openly accessible a comprehensive, multivariate time series
(MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather …

We propose a forecasting approach for solar flares based on data from Solar Cycle 24,
taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics …

In this paper we present several methods to identify precursors that show great promise for
early predictions of solar flare events. A data preprocessing pipeline is built to extract useful …

We present a long short-term memory (LSTM) network for predicting whether an active
region (AR) would produce a ϒ-class flare within the next 24 hr. We consider three ϒ …

Adverse space-weather effects can often be traced to solar flares, the prediction of which
has drawn significant research interests. The Helioseismic and Magnetic Imager (HMI) …

The European Union funded the FLARECAST project, that ran from January 2015 until
February 2018. FLARECAST had a research-to-operations (R2O) focus, and accordingly …

A deep learning network, long short-term memory (LSTM), is used to predict whether an
active region (AR) will produce a flare of class Γ in the next 24 hr. We consider Γ to be≥ M …

We present a case study of solar flare forecasting by means of metadata feature time series,
by treating it as a prominent class-imbalance and temporally coherent problem. Taking full …

We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called
support vector machine (SVM), and four years of data from the Solar Dynamics …

Abstract Space weather forecasting is very important, and the prediction of space weather,
especially for solar flares, has increasingly attracted research interests with the numerous …