## Time SeriesThis third edition of Time Series is a thorough revision of the classic text by the late Sir Maurice Kendall. The subject has undergone dramatic changes in the last 20 years, and Keith Ord here presents an up-to-date treatment that uses some of the classical methods, such as data-analytic devices, before considering modern approaches to model building. The mathematics is kept at a reasonable level to make the book accessible to undergraduates and postgraduate researchers in many applied fields, such as economics, heliology, oil prospecting, and management science. Both ARIMA and structural approaches to model building are discussed, and the work now includes worked examples using real and simulated data. End-of-chapter exercises have been added, along with 16 data sets for further analysis. The text also offers a discussion of spectral methods, including fast Fourier transforms, intervention analysis, and transfer function models. An introduction to multiple time series is provided, and new sections describe traditional forecasting procedures, comparative evaluations, and automatic model selection procedures. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Common terms and phrases

adjustment airline analysis approach appropriate ARIMA assume assumptions autocorrelation becomes Chapter clearly coefficients component computed consider correlations corresponding defined dependence described determine developed differencing discussion distribution effects equations error estimates examine Example Exercise expected expressions extended Finally fitted follows forecast frequency FT index function further given gives identified indicate intervals intervention known leading linear mean method model selection months moving average noted observations obtain original PACF parameters pattern peak performance periods plot points polynomial possible problem procedures produce quarter random regression removal represent require residuals SACF sample scheme seasonal Section selection sheep shown shows similar simple smoothing spectrum squares stationary statistics structural suggested Table transfer function trend turning usually values variables variance weights XXXX XXXXX yields zero