Introductory Time Series with R (Use R!)
| Author | : | |
| Rating | : | 4.52 (672 Votes) |
| Asin | : | 0387886974 |
| Format Type | : | paperback |
| Number of Pages | : | 256 Pages |
| Publish Date | : | 2015-06-07 |
| Language | : | English |
DESCRIPTION:
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 book gives you a step-by-step introduction to analysing time series using the open source software R. By using R, the whole procedure can be reproduced by the reader. Finally, the model is used to analyse observed data taken from a practical application. Each time series model is motivated with practical applications, and is defined in mathematical notation. This sequence enhances understanding of both the time series model and the R function used to fit the model to data. All the data sets used in the book are available on the website staff.elenat/Paul-Cowpertwait/ts/.The book is writt
ML said Sample Data Location change. The website for the sample data in the book has changed. Search under Paul Cowpertwait and you will find the new location. I was thinking I had to return the book because I could not find the sample data, but luckily I found it before I returned it. The power of the book is in the examples, so be aware of the change and it will spare you a little frustration.. "Some exercises and code does not work correctly as written" according to Scott G.. I like this book, but some of the exercises and code in the book is problematic such as exercise Some exercises and code does not work correctly as written Scott G. I like this book, but some of the exercises and code in the book is problematic such as exercise 4.6 and exercise 5.2. I had to fix the harmonic series code to get it to work, and there was still some code that still did not work. At times, it was difficult to follow. There were some enlightening exercises that helped me understand the content better.. .6 and exercise 5.2. I had to fix the harmonic series code to get it to work, and there was still some code that still did not work. At times, it was difficult to follow. There were some enlightening exercises that helped me understand the content better.. "A great book on R!" according to Pd Farleigh. This is a cracking book on applying R to time series analysis. The best parts of the book are all of the worked examples, the accompanying data sets and several different ways to calculate seasonality.The book is better than most on time series, because it does not neglect the de-trending process needed to get stationery residuals. If you use just the lm() command in R to do this before, then the real gem in this book is the advice to use the gls() command from the nlme library instead (to get the confidence intervals right).Overall, a very good book that is applied to R but has enough mathema
Both authors have extensive experience of teaching time series to students at all levels. Paul Cowpertwait is an associate professor in mathematical sciences (analytics) at Auckland University of Technology with a substantial research record in both the theory and applications of time series and stochastic models. Andrew Metcalfe is an associate professor in the School
…” (Journal of Statistical Software, January 2010, Vol. … The book is written for students with knowledge of a first-year university statistics course in New-Zealand and Australia, but it also might serve as a useful tools for applied researchers interested in empirical procedures and applications which are not menu driven as it is the case for most econometric software packages nowadays.” (Herbert S. Maindonald, International Statistical Review, Vol. Data sets used throughout the book are available in a web site or come with base R or the R packages used. 1179, 2010). … The mathematical theory is remarkably complete &h