STATS 726 Time Series

Below description edited in year: 2007

Points: 15

Prereqs: STATS 210 and either STATS 320 or STATS 325. STATS 201/8 is recommended

For Advice: David Scott (Email: d.scott@auckland.ac.nz | extn: 85055)

Taught: Second Semester City

STATS 726 provides a general introduction to the theory of time series and prediction including stationary processes, moving average and autoregressive (ARIMA) models, modelling and estimation in the time domain, seasonal models, forecasting, spectral analysis and bivariate processes. This foundation course is particularly suitable for students in economics and finance, and in the engineering and physical sciences.

Specific topics covered include: the basic theory of stationary processes; spectral or Fourier models; AR, MA and ARMA models; linear filtering; time series inference; prediction and other topics such as the sampling of continuous time processes.

Disclaimer:
Although every reasonable effort is made to ensure accuracy, this information for the course year (2008), is provided as a general guide only for students and is subject to alteration. All students enrolling at the University of Auckland must consult its official document, the University of Auckland Calendar, to ensure that they are aware of and comply with all regulations, requirements and policies.