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Solar flare forecasting research using machine learning (ML) has focused onhigh resolution magnetogram data from the SDO/HMI era covering Solar Cycle 24and the start of Solar Cycle 25, with some efforts looking back to SOHO/MDI fordata from Solar Cycle 23. In this paper, we consider over 4 solar cycles ofdaily historical magnetogram data from multiple instruments. This is the firstattempt to take advantage of this historical data for ML-based flareforecasting. We apply a convolutional neural network (CNN) to extract featuresfrom full-disk magnetograms together with a logistic regression model toincorporate scalar features based on magnetograms and flaring history. We usean ensemble approach to generate calibrated probabilistic forecasts of M-classor larger flares in the next 24 hours. Overall, we find that includinghistorical data improves forecasting skill and reliability. We show that singleframe magnetograms do not contain significantly more relevant information thancan be summarized in a small number of scalar features, and that flaringhistory has greater predictive power than our CNN-extracted features. Thisindicates the importance of including temporal information in flare forecastingmodels.

Probabilistic solar flare forecasting using historical magnetogram data