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tokens: X-ray fluxes. A flare is defined using the following algorithm (Ryan et al. 2016 ; NOAA GOES X-ray flux 1 1 https://www.swpc.noaa.gov/products/goes-x-ray-flux ): (1) the begin time of an X-ray event is defined as the first minute, in a sequence of 4 minutes, of steep monotonic increase in 0.1–0.8 nm flux, (2) the flux at the end of the fourth minute is at least 40% greater than the flux in the first minute, (3) the X-ray event peak time is taken as the minute of the peak X-ray flux, and (4) the end time is the time when the flux level decays to a point halfway between the maximum flux and the background level. We apply the algorithm to GOES 10 one-minute X-ray flux data, from 1998 August to 2006 May, to recognize major solar flares. In order to select reliable solar flare events, we remove multiple-peak events and pre-flare phase events by simple automatic process and visual inspection. As a result, we select 845 major solar flare events. Soft X-ray fluxes from 26 minutes before flare start time to 3 minutes after it are used for input and those of following 30 minutes are used for target. Our data set is constructed using the sliding window method. The window slides from 3 minutes after flare start time to its peak time. We randomly split the data set into training and test sets since flare X-ray profiles can be assumed to be mostly independent one another in that flares have a Poisson distribution in time (Rosner Vaiana 1978 ; Moon et al. 2001 ; ?, ?). The data set is separated by each flare event to avoid that the data from the same flare event are used for training and test both. It is noted that when we divide the data set chronologically, the number of major flares in the test data set is too small. In order to reduce the effect of test data selection and random initial weights, we consider 10-fold cross-validation. As a result, 6227–6381 data (90% of events; 69 X-class and 691 M-class) are used for training and 654–808 data (10% of events; 8 X-class and 77 M-class) for test. The difference in data number is due to different lengths of flare events in each cross-validation set. 3. Model 3.1. Methods LSTM is one of the advanced recurrent neural networks (Hopfield 1982 ) that is good to learn time dependence in sequence. LSTM have been widely employed in various fields such as machine translation (Sutskever et al. 2014 ; Wu et al. 2016 ), speech recognition (Graves Schmidhuber 2005 ), and time-series prediction (Duan et al. 2018 ; Tan et al. 2018 ). LSTM layer has a cell state candidate and three gates, to transfer hidden state from prior time step to next time step. The time step means a sequential order of elements in time-series data. Details of the LSTM calculation are described below. where f t represents the forget gate, i t represents the input gate, represents the cell state candidate, c t represents the cell state, o t represents the output gate, and h t represents the hidden state at time step t ; X is input data; W terms