Autoregressive models & deep learning(rnn-lstm) models both are used for time series prediction. As we choose the 'look back' for lstm's, provision to choose optimal lag by viewing acf-pacf plot or AR coefficient is also there for Autoregressive models.
- When can we know that we have to use deep learning instead of other time series methods?
- How do AR model & LSTM model differ? I know lstm is composed with gates & inherent memory blocks unlike AR, but theoretically why should not I use AR models?
On an other note, i would also like to know why RMSProp optimizer is used(or better) in recurrent neural networks?