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IB98E - Forecasting

  • Module code: IB98E
  • Module name: Forecasting
  • Department: Warwick Business School
  • Credit: 15

Content and teaching | Assessment | Availability

Module content and teaching

Principal aims

The module provides an introduction to current quantitative forecasting methods, and its overall aim is to develop practical competence in their use. The module concentrates on models for short term forecasting, as these illustrate all the basic principles of analysing, comparing and extrapolating different models.

Principal learning outcomes

Subject Knowledge and Understanding: Demonstrate a firm understanding of the ideas and principles underlying the most commonly used forecasting models.Cognitive Skills: Comprehensively understand the relevant issues and measures available to aid selection of the most suitable models.Key/Subject Specific/Professional Skills: Apply several forecasting models to real data sets, conducting a range of analyses using appropriate software; Produce reports investigating real data sets and be able to report on the findings from a piece of modelling and analysis in practical terms; Research a relevant topic using available literature sources and present cogently and effectively at masters level.

Timetabled teaching activities

Total contact hours: 24 hours. Lectures: 24 hours. Module duration (weeks, if applicable): 4 days x 6 hours per day weeks. Other (please describe):e.g. distance-learning, intensive weekend teaching: 2- 2 day block.

Departmental link

Module assessment

Assessment group Assessment name Percentage
15 CATS (Module code: IB98E-15)
D (Assessed/examined work) Group coursework (1500 words) 25%
  2 hour examination (April) 75%

Module availability

This module is available on the following courses:



Optional Core


  • Postgraduate Taught Business Analytics and Consulting (N1N2) - Year 1
  • Postgraduate Taught Business Analytics and Consulting (N1N2) - Year 2
  • Postgraduate Taught Business Analytics (N1N3) - Year 1
  • Postgraduate Taught Business Analytics (N1N3) - Year 2