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◄Faculty of Business and Economics►

AA037012 TIME SERIES ANALYSIS
3 Credits

Course Learning At the end of this course, students are able to:
Outcomes
(1) Examine time series patterns of data (graphically and
quantitatively);
(2) Construct forecasting models that incorporate
correlated error structures;
(3) Compare the forecasting performance of the different
models developed for a given set of data; and
(4) Explain the results arising from the application of time
series analysis in various fields.

Synopsis of Course This course exposes students to the study of time series
Contents data. It focuses on the use of statistical models (such as
classical decomposition, exponential smoothing, least
squares, ARIMA) for forecasting. Students learn to assess
and select an appropriate model from among different
possible models for a given set of data. The use of statistical
software to analyse data ensures that the students learn the
nuances of modelling correlated error structures.

Assessment Weightage Continuous Assessment: 60%
Final Examination: 40%

Medium of Instruction English

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