# Multi-Label time-series classification with LSTM: large performance decrease for longer periods

I have daily data on event occurences, so for each day I have a vector like [1, 0, 1] indicating that on this day event one and three occured, but event two did not occur. I want to train a model to take data from the past number of days (n_days) and to then predict the event occurences for the next day.

I believe this problems falls into the category of multi-label classification. Moreover, the data that I use has a clear time series structure. This is because events typically happen periodically (e.g. event 1 may happen every 30 days, and event 2 may happen every other day). I believe that an LSTM should be a good fit for this problem, as it can be used for multi-label classification and should also be very good for time series.

I coded a very simple LSTM to test its performance on a toy dataset, where I can create obvious patterns and feed a bunch of data, so that the LSTM should perform well. I will put it here:

model = keras.Sequential()
layers.LSTM(n_events, input_shape=(x_train.shape[1], x_train.shape[2]))
)