Introductory description

The module provides an introduction to current quantitative forecasting methods, and its overall aim is to develop practical competence in their use.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Introduction to Forecasting
Time Series and their Components
Regression with Time Series Data
Smoothing methods
The Box-Jenkins (Arima) Methodology

Learning outcomes

By the end of the module, students should be able to:

• Research a relevant topic using available literature sources and present cogently and effectively at masters level.
• Demonstrate a firm understanding of the ideas and principles underlying the most commonly used forecasting models.
• Comprehensively understand the relevant issues and measures available to aid selection of the most suitable models.

Indicative reading list

Amstrong, J. S. (Ed.) (2001). Principles of Forecasting: a handbook for researchers and practitioners. Kluwer.
Chatfield, C. (2004) The Analysis of Time Series. Chapman & Hall.
Hanke, J.E. and Wichern, D.W. (2009). Business Forecasting. Pearson Prentice Hall.
Makridakis, S. Wheelwright S.C. and Hyndman, R.J. (1998). Forecasting, Methods and Applications, 3rd ed. John Wiley & Sons.
Ord, K. and Fildes, R. (2013). Principles of Business Forecasting, International ed, South-Western Cengage Learning.
Online textbook:
Forecasting: Principles and Practice: https://www.otexts.org/fpp
Website:
http://www.forecastingprinciples.com/

Subject specific skills

Apply several forecasting models to real data sets, conducting a range of analyses using appropriate software.
Investigate real data sets and be able to report on the findings from a piece of modelling and analysis in practical terms.

Transferable skills

Group working skills
Communication skills
Numeracy and IT skill