# What is a feasible sequence length for an RNN to model?

I'm looking into using a LSTM (long short-term memory) version of a recurrent neural network (RNN) for modeling timeseries data. As the sequence length of the data increases, the complexity of the network increases. I am therefore curious what length of sequences would be feasible to model with a good accuracy?

I would like to use a relatively simple version of the LSTM without any difficult to implement state-of-the-art approaches. Each observation in my timeseries would likely have 4 numeric variables and the number of observations would be around 100.000 to 1.000.000.

• Say that I need to do sentiment analysis, which is a many-to-one approach (see karpathy.github.io/2015/05/21/rnn-effectiveness). Each of these senteces are very long (>200 words). If I only backpropagate-through-time the usual 35 steps, wouldn't that be an issue? Since it's supervised learning I assume that it can only backpropagate when it "hits" the binary classification target, y. In this way how would the RNN ever adjust weights based on anything prior to the 35 steps selected for BPTT? – pir Aug 17 '15 at 12:45