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 youa step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, andis defined in mathematical notation. Once the model has been introduced it isused 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 modelis 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 areavailable on the website http://www.massey.ac.nz/~pscowper/ts. Easy to read Motivated with real cases addressing contemporary issues Detailed explanations of the use of R for time series analysis INDICE: 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.
- ISBN: 978-0-387-88697-8
- Editorial: Springer
- Encuadernacion: Rústica
- Páginas: 254
- Fecha Publicación: 01/06/2009
- Nº Volúmenes: 1
- Idioma: Inglés