Choosing the Length of Time Steps in Recurrent Neural Network

Question

I have a time-series regression problem with a single predictor and a real-valued output, and I would like to use LSTM recurrent neural network to model the data. How should I choose the number of time steps in my model? Is there any upper limit for the length of LSTM layer?

Answer 1

LSTMs can be challenging to use when you have very long input sequences and only one or a handful of outputs. A reasonable limit of 250-500 time steps is often used in practice with large LSTM models.

Answer 2

How should I choose the number of time steps in my model?

It entirely depends on the task at hand, in short the time series frequency determines this if the data you have is at the following interval minutes then 60 , hours then 24 ,month then 12 and so on.

Simply stating At what level you need your predictions

Is there any upper limit for the length of LSTM layer?

It depends on the quantum of data . A reasonable limit of 250-500 time steps in good to go. Long input sequences may result in vanishing gradients, and in turn, an unlearnable model