  We develop a mixed Long Short Term Memory (LSTM) regression model to predict
the maximum solar flare intensity within a 24-hour time window 0$\sim$24,
6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48
hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI)
Active Region Patch (HARP). The model makes use of (1) the Space-weather HMI
Active Region Patch (SHARP) parameters as predictors and (2) the exact flare
intensities instead of class labels recorded in the Geostationary Operational
Environmental Satellites (GOES) data set, which serves as the source of the
response variables. Compared to solar flare classification, the model offers us
more detailed information about the exact maximum flux level, i.e. intensity,
for each occurrence of a flare. We also consider classification models built on
top of the regression model and obtain better results in solar flare
classifications. Our results suggest that the most efficient time period for
predicting the solar activity is within 24 hours before the prediction time
using the SHARP parameters and the LSTM model.

Solar and Stellar Astrophysics