This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.
A good intro to time series. However, for people who are looking for something they can use to forecast thousands of entities automatically, I suggest the forecast package by Hyndman. I work for a large retailer and his package allows us to forecast demand for 100k+ SKUs without having to figure out the three things you really need to know about each 1) error, 2) trend, and 3) seasonality. If anyone is really interested in this area, I going straight to this paper first - http://www.jstatsoft.org/v27/i03/paper
Overall this is my favorite first book on time series, provided you already understand regression. The first half of this book is a very gentle and comprehensible introduction; the second half is a lightning tour of more advanced techniques which has impressively broad coverage but which will not be as easy to follow.
Excellent description with a lot of paradigms and graphs. It rpovides both mathematical quations and R coding examples. Essential for R users. I definitely recommend it. 5 stars.