Introductory Time Series with RThis 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. |
Contents
| 1 | |
Correlation | 26 |
Forecasting Strategies | 45 |
Basic Stochastic Models | 67 |
Regression | 90 |
Stationary Models | 121 |
Nonstationary Models | 137 |
LongMemory Processes | 159 |
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additive adjusted analysis applications appropriate approximately ARIMA autocorrelation calculate Chapter coefficients components consider correlation correlogram cycles defined deviation differenced distribution electricity equal Equation errors estimate example exchange rate Exercise expected expressed Figure filter fitted fitted model forecasts frequency function given gives increasing independent indicates input interval known length linear matrix mean measured method monthly months natural observation obtained output parameters period plot positive predicted production provides random walk realisation regression model remove residual series residuals result roots sample seasonal seasonal effects series model shows signal significant simulated sine smoothing spectrum squared standard stationary statistical stochastic temperature trend underlying unit usually values variables variance variation wave weighted white noise zero