IB98E-15 Forecasting
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 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.
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/
Research element
Research a relevant topic using available literature sources and present cogently and effectively at masters level
Interdisciplinary
Available to MMORSE students in the Statistics department as part of that course
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
Study time
Type | Required |
---|---|
Lectures | 9 sessions of 1 hour (6%) |
Online learning (independent) | 4 sessions of 1 hour (3%) |
Other activity | 14 hours (9%) |
Private study | 49 hours (33%) |
Assessment | 74 hours (49%) |
Total | 150 hours |
Private study description
Private study to include preparation for lectures, seminars and lab sessions
Other activity description
Laboratory/workshop sessions
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D2
Weighting | Study time | Eligible for self-certification | |
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Assessment component |
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Group coursework | 25% | 19 hours | No |
1500 words |
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Reassessment component |
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Individual assignment | Yes (extension) | ||
Assessment component |
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In-person Examination | 75% | 55 hours | No |
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Reassessment component is the same |
Feedback on assessment
Assessments are graded using standard University Postgraduate Marking Criteria and written feedback is provided. Feedback for individual essays includes comments on a marksheet. Overall percentage marks are awarded for examination performance and general examination feedback is provided to the cohort.
Courses
This module is Optional for:
- Year 1 of TIBS-N1N3 Postgraduate Taught Business Analytics
-
USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
- Year 3 of G300 Mathematics, Operational Research, Statistics and Economics
- Year 4 of G300 Mathematics, Operational Research, Statistics and Economics
This module is Option list C for:
- Year 4 of USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics