SEEM8102 - Forecasting and Control Using Regression, Time Series, and Dynamic Models

Offering Academic Unit
Department of Systems Engineering and Engineering Management
Credit Units
Course Duration
One Semester
University level mathematics
University level course in probability and statistics
Course Offering Term*:
Not offering in current academic year

* The offering term is subject to change without prior notice
Course Aims

This course aims to educate and to train students and other professionals in business, engineering, mathematics, economics, and statistics, to the principles and the methods for predicting, forecasting, and controlling, using probabilistic and statistical methods. It will start with an overview of methods for quantifying uncertainty, followed by methods of predicting binary outcomes. It will then discuss regression and time series based models, such as autoregressive-moving average processes, for predicting non-binary outcomes. This will be followed by a use of dynamic (or state-space/Kalman Filter) models for prediction and control. Theoretical underpinning will be emphasized and assignments will entail exercises as well as the analysis of data and/or the class participants.

Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 40%
Examination: 60%
Examination Duration: 2 hours
Detailed Course Information


Useful Links

Department of Systems Engineering & Engineering Management