A Deep-Learning Approach for Operation of an Automated Realtime Flare   Forecast
  Automated forecasts serve important role in space weather science, by providing statistical insights to flare-trigger mechanisms, and by enabling tailor-made forecasts and high-frequency forecasts. Only by realtime forecast we can experimentally measure the performance of flare-forecasting methods while confidently avoiding overlearning.   We have been operating unmanned flare forecast service since August, 2015 that provides 24-hour-ahead forecast of solar flares, every 12 minutes. We report the method and prediction results of the system. 
Yuko Hada-Muranushi, Takayuki Muranushi, Ayumi Asai, Daisuke   Okanohara, Rudy Raymond, Gentaro Watanabe, Shigeru Nemoto, Ka...
astro-ph.SR cs.LG