MS6219 - Predictive Modeling and Forecasting for Business | ||||||||||||
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| * The offering term is subject to change without prior notice | ||||||||||||
Course Aims | ||||||||||||
Throughout the course, students will develop a comprehensive understanding of various forecasting techniques. They will explore various smoothing methods, such as moving averages and exponential smoothing, to identify patterns and trends in time series data. Additionally, students will delve into Box-Jenkins models, a powerful approach for forecasting based on autoregressive integrated moving average (ARIMA) models. This will enable them to handle complex time series data and make accurate predictions. Moreover, students will learn about regression-based forecasting methods, emphasizing the use of regression analysis to model relationships between variables and generate forecasts. Through lectures, practical exercises, and case studies, students will gain hands-on experience in applying these techniques to real-world business scenarios. By the end of the course, students will possess the necessary skills to analyze historical data, build predictive models, and generate reliable forecasts to support decision-making in a business context. | ||||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||||
Continuous Assessment: 35% | ||||||||||||
Examination: 65% | ||||||||||||
Examination Duration: 3 hours | ||||||||||||
Detailed Course Information | ||||||||||||
| MS6219.pdf | ||||||||||||
Useful Links | ||||||||||||
| Department of Management Sciences | ||||||||||||
