Lecturer

Ms Kaye Marion, RMIT, Semester 1.

Syllabus

Examples, objectives, general approaches. Removing trend and/or seasonality. Stationary random processes; the autocorrelation function; the sample autocorrelation function; Bartlett's formula; testing for white noise. Best linear mean square prediction; forecasting stationary processes. ARMA processes and their autocorrelation functions; the partial autocorrelation function. Introduction to spectral analysis; linear filters; the spectral density of an ARMA process. Model building and forecasting with ARMA processes. Non-stationary and seasonal time series models.

Prerequisites

Basic undergraduate exposure to stochastic processes and statistical inference. A knowledge of elementary real and complex analysis.

Generic skills

 

References

Text

• Brockwell P.J. and Davis R.A. (1996) Introduction to Time Series and Forecasting, Springer-Verlag, New York.

References

• Brockwell P.J. and Davis R.A. (1991) Time Series: Theory and Methods, 2nd ed., Springer Verlag.
• Chatfield, C. (1984) The Analysis of Time Series: an Introduction, 3rd ed., Chapman and Hall.
• Fuller, W.A. (1976) Introduction to Statistical Time Series, John Wiley.
• Cryer, J.D. (1988) Time Series Analysis, Duxbury.
• Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control, 2nd ed., Holden-Day.
• Priestley, M.G. (1981) Spectral Analysis and Time Series, Vol 1 Academic Press.

Last updated: 30 October 2002.