Contents -- Preface -- 1.Time Series Data -- 2.Correlation -- 3.Forecasting Strategies -- 4.Basic Stochastic Models -- 5.Regression -- 6.Stationary Models -- 7.Non-stationary Models -- 8.Long-Memory Processes -- 9.Spectral Analysis -- 10.System Identification -- 11.Multivariate Models -- 12.State Space Models -- References -- Index.

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 http://staff.elena.aut.ac.nz/Paul-Cowpertwait/ts/.
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.