Elements of Time Series III

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Description

Objective

Content

This course provides an introduction to time series analysis. Topics covered in this course include descriptive methods of analysis: plots, smoothing, differencing, the autocorrelation function, the correlogram and the variogram; the periodogram; estimation and elimination of trend and seasonal components. Stationary processes, modelling and forecasting with autoregressive moving average (ARMA) models. Additional topics will be selected from Spectral analysis: the fast Fourier transform, periodogram averages and other smooth estimates of the spectrum; time-invariant linear filters. Nonstationary and seasonal time series models; ARIMA processes: identification, estimation and diagnostic checking; forecasting, including extrapolation of polynomial trends, exponential smoothing, and the Box-Jenkins approach.

Zudi Lu Lecturer for this course

Delivery

24 hours lectures, tutorials, practical

Assessment

2 hour exam and assignments

Graduate attributes

• Applying mathematical knowledge (1)
• Interpreting data and drawing conclusions (2)
• Formulating mathematical problems (3)
• Problem solving skills (4)
• Flexibility in working environment (5)

Linkage past

MATHS 1012 (Pass Div I) or MATHS 2004 (Pass Div I). One of STATS 1000(Pass Div I), STATS 1004 (Pass Div 1), STATS 2004 (Pass), APP MTH 2009(Pass), STATS 2001 (Pass). Assumed knowledge is a statistical background such as in any of the Level II Statistics courses.

Linkage present

No present linkages have been noted.

Linkage future

This course is not recorded as prequisite for other courses.

Restrictions

Cannot be counted with STATS 3005 Time Series III

Recommended text

None.