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◄Faculty of Business and Economics►

AA037007 TIME SERIES ANALYSIS
3 Credits

Learning Outcomes At the end of the course, students are able to:
(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 data.
Contents 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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