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.