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A deep learning network, Long-Short Term Memory (LSTM) network, is used inthis work to predict whether the maximum flare class an active region (AR) willproduce in the next 24 hours is class $\Gamma$. We considered $\Gamma$ are $\geM$, $\ge C$ and any flare class. The essence of using LSTM, which is arecurrent neural network, is its capability to capture temporal information ofthe data samples. The input features are time sequences of 20 magneticparameters from SHARPs - Space-weather HMI Active Region Patches. We analyzedactive regions from June 2010 to Dec 2018, using the Geostationary OperationalEnvironmental Satellite (GOES) X-ray flare catalogs and label the data sampleswith identified ARs in the GOES X-ray flare catalogs. Our results (i) showsconsistent skill scores with recently published results using LSTMs and betterthan the previous work using single time input (eg. DeFN) (ii) The skill scoresfrom the model show essential differences when different years of data waschosen for training and testing.

Predicting solar flares with machine learning: investigating solar cycle dependence