| DC Field | Value | Language |
|---|
| dc.contributor.author | Cowpertwait, Paul S.P. | - |
| dc.date.accessioned | 2021-04-19T07:20:43Z | - |
| dc.date.available | 2021-04-19T07:20:43Z | - |
| dc.date.issued | 2009 | - |
| dc.identifier.isbn | 978-0-387-88698-5 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/76 | - |
| dc.description | The book is based around practical applications and generally follows a
similar format for each time series model being studied. First, there is an
introductory motivational section that describes practical reasons why the
model may be needed. Second, the model is described and defined in math-
ematical notation. The model is then used to simulate synthetic data using
R code that closely reflects the model definition and then fitted to the syn-
thetic data to recover the underlying model parameters. Finally, the model
is fitted to an example historical data set and appropriate diagnostic plots
given. By using R, the whole procedure can be reproduced by the reader,
and it is recommended that students work through most of the examples. 1
Mathematical derivations are provided in separate frames and starred sec | en_US |
| dc.description.abstract | R has a command line interface that offers considerable advantages over menu
systems in terms of efficiency and speed once the commands are known and the
language understood. However, the command line system can be daunting for
the first-time user, so there is a need for concise texts to enable the student or
analyst to make progress with R in their area of study. This book aims to fulfil
that need in the area of time series to enable the non-specialist to progress,
at a fairly quick pace, to a level where they can confidently apply a range of
time series methods to a variety of data sets. The book assumes the reader
has a knowledge typical of a first-year university statistics course and is based
around lecture notes from a range of time series courses that we have taught
over the last twenty years. Some of this material has been delivered to post-
graduate finance students during a concentrated six-week course and was well
received, so a selection of the material could be mastered in a concentrated
course, although in general it would be more suited to being spread over a
complete semester. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Time Series | en_US |
| dc.subject | Time Series with R | en_US |
| dc.title | Introductory Time Series with R | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | ARTS & SCIENCE
|
