I am building an LSTM to attempt to learn the trend historic trend of some time-series data set (e.g. the daily share price of a company). When training my network, I am taking batches of size 1, each consisting of 25 sequential daily closing prices, where it then makes a prediction for the price on the 26th day.
Say the first sequence in an epoch used to train begins at t=0 and ends at t=24. I am using a stateful LSTM and hence taking the output state of one batch and inputting it to the state of the next batch, does this mean that my next batch must be [t=25, t=49]? Can I instead "slide" each batch by 1 time step so that the 2nd batch is [t=1, t=25], or does this defeat the purpose of passign the state between batches?