I am looking for resources on the techniques for time series forecasting. It seems that there are three approaches, listed below in the order of their machine learning-ness (and correspondingly their greediness for data):
- ARIMA and GARCH models
- Hidden Markov Models (HMMs)
- Neural networks: RNNs, LSTMs, GRUs
In terms of sources ARIMA/GARCH do not pose problems - there is wealth of books, notes, tutorials, etc. HMMs are well covered as well, but I haven't seen yet anything where they would be applied to time series. Finally, the resources on RNN/LSTM/GRU seem to be scarce, perhaps due to relative novelty of this domain.
I will appreciate books/articles recommendations regarding these techniques and their application to time series. If you want to post your own overview of the subject, it will be greatly appreciated as well.