Índice

Bibliografía: p. 247-248

Yearly global mean temperature and ocean levels, daily share prices, and the signals transmitted back to earth by the Voyager space craft are all examples of sequential observations over time known as time series. 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 confirms understanding of both the model and the R routine for fitting it to the data. Finally, the model is applied to an analysis of a historical data set. 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://www.massey.ac.nz/p̃scowper/ts. Easy to read Motivated with real cases addressing contemporary issues Detailed explanations of the use of R for time series analysis

Índice: Time series data.- Correlation.- Forecasting strategies.- Basic stochastic models.- Regression.- Stationary models.- Non-stationary models.- Long memory processes.- Spectral analysis.- System identification.- Multivariate models.- State space models.