notes - Time Series Analysis Dr. Gavin Shaddick...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Time Series Analysis Dr. Gavin Shaddick g.shaddick@bath.ac.uk www.bath.ac.uk/ masgs Department of Mathematical Sciences University of Bath Bath BA2 7AY 2004 1 Books Most suitable books for the course: (* Recommended) Chatfield, C. The analysis of time series. Chapman and Hall. (*) Harvey, A.C. Time series models. MIT Press. (*) Diggle, P.J. Time series; a biostatistical introduction. Oxford. Box, G.E.P. & Jenkins, G.M. Time series analysis; forecasting and control. Holden Day. Priestley, M.B. Spectral analysis and time series. Academic Press. 2 Course Outline 1. Introduction Examples; correlations. 2. Stationary Stochastic Models Stationarity; lag; autocovariance and autocorrelation functions; 3. Models for stationary time series Wold decomposition theorem, White noise; q-th order moving average (MA); p-th order autoregression (AR); ARMA process; invertibility; the backshift operator 4. Non-stationary processes and ARIMA models Residuals; differencing. 5. Model specification Identification; estimation of the autocorrelation function; PACF; nonstationarity; estimation; verification. 6. Forecasting MMSEP; forecasting using ARIMA models. 7. Seasonal models Seasonal MA, AR, ARMA, ARIMA 8. Structural time series models Local level model; state-space models; kalman filter 3 R For the computational elements of this course I intend this year to use a package called simply R. This package has a number of compelling advantages: (i) It is a grown-up package. In fact, it is virtually identical (from the users point of view) to the well-known package S-plus; either package can be considered as the standard workplace of the modern statistician, and has built-in analytical and graphical capa- bilities to handle effectively every possible statistical analysis you will ever need. If you are doing other statistical courses this year, look around in R for ways of doing the calculations of those courses. (ii) It has a full on-line html help browser, with search facilities. (iii) It costs nothing! Visit one of the R web sites (for example, there is one nearby at http://www.stats.bris.ac.uk/R/) to download the latest ver- sion (R-1.8.1). (iv) Various datasets are integral to the package, and we shall largely (though not exclusively) be using these for illustration throughout the course. As a reference for the use of R, you can print out the documentation An Intro- duction to R from the web site. You can also go and look in Venables, W. N. and Ripley, B. D. Modern Applied Statistics with S-PLUS (ISBN 0 387 98825 4). In the past, this course has used Minitab, which is very easy to use, but is slow and inflexible; some of the basic computations required in time series are not part of the base package, and have to be programmed separately. The use of R is via command line rather than menu-driven, and while this takes a while to get used to, it is a small price to pay to get on board a package which you will use throughout your professional life....
View Full Document

Page1 / 71

notes - Time Series Analysis Dr. Gavin Shaddick...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online