I am planning to use neural networks for time series analysis and forecasting and i was looking for a reference or any textbook i can use for that purpose. It would be great if it has practical R or python code with it.
Well you can use neural networks for time series forecasting. I am not sure about the analysis part. You can use recurrent neural network (RNN) , LSTM (long short term memory) a type of RNN, for your problem. Recurrent neural networks are Artificial neural networks with cycles. They are the natural architecture for learning over sequences.
Few points that might be helpful for you to start with:
1) Consult a book on RNN and LSTM. The book discusses in detail about the problems that RNN and LSTM are being used to solve.
2) ALso have a look at some great blogs on the web. This blog is one of the most popular for understanding the recurrent neural network and LSTM in most simplistic ways. This is highly recommended.
3) Go through research papers that compare traditional time series approaches like ARIMA, VAR, ARIMAX etc to LSTM, RNN, GRU etc.
Ex- 1) Comparison of multivariate forecasting technique VAR (Vectpor Autoregression) with RNN. This paper will give you a perspective of how traditional statistical model like VAR compare to neural network.
2) This paper is about forecasting economics and financial time series. It compares ARIMA to LSTM.
4) Last but not the least. This tutorial gives a fair amount of idea on how to prepare time series data for forecasting and also familiarizes with using LSTM using keras. It contains reproducible example with code. This will be very useful if you quickly want to get your hands dirty with implementing LSTM.