302387 (v.2) Time Series Modelling 404

Area:	Department of Mathematics and Statistics
Credits:	25.0
Contact Hours:	3.0

The tuition pattern below provides details of the types of classes and their duration. This is to be used as a guide only. For more precise information please check your unit outline.

Lecture:	3 x 1 Hours Weekly
Anti Requisite(s):	304193 (v.3) Time Series Modelling 504 or any previous version
Prerequisite(s):	302315 (v.2) Mathematical Statistics 202 or any previous version

Syllabus:	Exponential smoothing methods to forecast non-seasonal and seasonal time series. Stochastic time series models and fundamental concepts. Invertibility and stationarity, autocorrelation and partial autocorrelation, identification and estimation in non-seasonal ARIMA models, forecasting and diagnostic checking. Seasonal time series models. Intervention analysis and outlier detection. Multivariate time series, vector ARMA and state-space modelling.

To ensure that the most up-to-date information about unit references, texts and outcomes appears, they will be provided in your unit outline prior to commencement.

Field of Education:	010101 Mathematics
Funding Cluster:	04 - Mathematics, Statistics
SOLT (Online) Definitions*:	Not Online *Extent to which this unit or thesis utilises online information
Result Type:	Grade/Mark
Availability
Availability Information has not been provided by the respective School or Area. Prospective students should contact the School or Area listed above for further information.

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