Learning material about time series 
Possible Duplicate:
Books for self-studying time series analysis? 

I am new to time series modelling altogether. But I am aware about regression modelling and some data mining algorithms like decision trees.
I want to learn time series from scratch. My background is Mathematics. Please suggest me any good book/material/web resource for step by step learning with case studies.
Thanks
 A: I have yet still not found the time series book which I like. Here is the list of books which I found very useful:


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*Time Series Analysis by J. D. Hamilton. This is the book which contains practically everything.

*Applied econometric time series by W. Enders. Classical reference

*Introductory econometrics for finance by Ch. Brooks. I use this book for teaching time-series to students with little mathematical background. Nevertheless it is a good book for getting the ideas without too much mathematical detail.
A: If you're coming from a mathematics background, and you want to learn time series, it's hard to go wrong with a combination of:


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*The Analysis of Time Series (Chatfield): introduction at the undergraduate level

*Fourier Analysis of Time Series (Bloomfield): introduction to Fourier methods at the undergraduate level


and after you've gone through those two and learned the basics, proceed to:


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*Time Series: Theory and Methods (Brockwell & Davis): excellent high-level undergraduate / starting graduate-level book

*Spectral Analysis and Time Series (Priestley): excellent graduate-level text


and if you become interested in spectrum estimation, the best book I'm aware of is:


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*Spectral Analysis for Physical Applications (Percival and Walden): more of an engineering flavour, but lots of great examples and carefully written algorithms that you can turn into code.


When I want to look up something I've seen before in classical time series methods, I mostly use Priestley. It's not an easy read by any means, but it's very well written, and you can go back to it and learn new things every time. Since you're coming from a mathematics background, you shouldn't have too much issue with any of the probabilistic notation, especially if you've had some measure theory. If I'm reviewing an algorithm for spectral methods, I use  Percival & Walden: it's the only good book I'm aware of that covers modern spectrum estimation techniques without diverging too strongly into wavelets or time-frequency methods.
I would encourage you to stay away from focused books on econometrics or any area of time series where the focus is on one particular area, as nonstandard notation and terminology tends to develop within these subfields. If it's your first approach to time series, start with a couple of good general undergraduate books (1 and 2 are decent, and have lots of examples that you can work through on your own with R). Only after you know the basics should you venture into the world of specific subfields and read books there.
