Module overview
This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.
Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. This module gives you a thorough understanding of various statistical methods for forecasting, in particular time-series methods that have wide applications in business.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts, and sometimes it is necessary to provide distributional rather than point forecasts. As such, an introduction to methods for distributional forecasting will also be provided.
As forecasting often requires huge amount of data, both for training and testing the models, and the required formulae and equations are often complicated, it is essential to implement forecasting methods using a proper statistical package. As such training will be provided on using R and SAS package for implementing forecasting methods.
Linked modules
Pre-requisite: MANG2062
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- the capabilities as well as limitations of quantitative-based forecasting methods;
- different fields of application of time series analysis and forecasting;
- the importance of incorporating uncertainty in forecasting.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- self-manage the development of learning and study skills;
- plan and control effectively for successful completion of a personal workload;
- communicate effectively, in both oral and written form, using and justifying argument within reports and presentations.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- use advanced statistical tools to fit and analyse such models to data;
- choose the most appropriate forecasting method using various types of information criterion.
- formulate time series models including exponential smoothing methods, ARIMA methods, and innovations state space models;
Syllabus
The topics covered in this module will include:
- Introduction to Forecasting: quantitative and qualitative methods;
- Time series models: decomposition, analysis and removal of trend, seasonality, and cycle;
- Exponential Smoothing Methods: Single Exponential, Holt and Holt-Winters Methods;
- Box-Jenkins Methods for ARIMA models;
- Simple and Multiple Regression Techniques;
- Introduction to Innovations State Space models.
Learning and Teaching
Teaching and learning methods
Teaching methods include:
- Lectures
- Interactive case studies
- Problem-solving activities
- Computer labs
- Directed reading
- Private/guided study.
Learning activities include:
- Introductory lectures
- An assignment (individual written coursework)
- Case study/problem solving activities
- In class debate and discussion
- Private study
- Use of video and online materials
Type | Hours |
---|---|
Lecture | 24 |
Supervised time in studio/workshop | 12 |
Follow-up work | 24 |
Preparation for scheduled sessions | 20 |
Completion of assessment task | 70 |
Total study time | 150 |
Resources & Reading list
General Resources
SAS Base Software. R and SAS Software. This module will require the weekly use of a computer lab equipped with the latest version of SAS Base Software and R programming Language. R is open source programming software and you can download in your computer freely. You can install the SAS software from iSolutions.
Textbooks
Hyndman, R.J. and Athanasopoulos, G (2013). Forecasting: Principles and Practice. New York: Wiley.
Assessment
Formative
Formative assessment description
Report Student presentationSummative
Summative assessment description
Method | Percentage contribution |
---|---|
Report | 100% |
Referral
Referral assessment description
Method | Percentage contribution |
---|---|
Report | 100% |
Repeat
Repeat assessment description
Method | Percentage contribution |
---|---|
Report | 100% |
Repeat Information
Repeat type: Internal & External