Introductory description

Decision making is an important part of business and, indeed, of life in general. Every day we make choices using a combination of opinions, facts, and 'hard evidence' in order to try and achieve better outcomes. In an uncertain environment, the success of an organisation or a policy depends upon the ability of decision makers and managers to prepare for the future.

This module will give you a thorough introduction to the field of time series analysis and will equip you with a tool bag of quantitative techniques that you can use to make forecasts. You will learn enough theory about the different methods to enable you to make sensible decisions about the best techniques to use in any given situation. However, there will be a strong focus on the practical side of forecasting, the 'art', in order to make you competent users of standard techniques.

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 Business Forecasting, Time Series and their components
An introduction to some of the main foundational time series forecasting methods and diagnostics, for example: Autocorrelation Function, 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 time series forecast models and diagnostics.
• Comprehensively understand the relevant issues and measures available to aid selection of the most suitable models.

Indicative reading list

Reading lists can be found in Talis

Research element

The group work asks students to research a given topic in order to widen their understanding of business forecasting techniques and processes.

Subject specific skills

Apply and assess different forecast 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
Written communication skills
Numeracy and IT skills