I'm using a stateful LSTM for stock market analysis, and I have varying amounts of data for each stock, ranging from 20 years to just a few weeks (i.e. for newly listed stocks).
I use 3 years of data as a minimum for training as I want to create some state within the network. I set a year as my sequence length, so if I have 12 years of data then I will submit 4 batches with 3 sequences in each. Only after I've submitted all batches do I then reset the network state ready for the next stock.
But is there any issue training with differing number of sequences? E.g. if I train with a company that has 20 years of data then the network will build up much more state than a company that I only have 3 years of data.