This module runs in Term 2 and is concerned with the theory and practice of short-term forecasting, using both data and subjective information. The course focuses on Dynamic Linear Models (DLM). DLM's are a class of Bayesian Forecasting Models which generalise linear regression models and static statistical linear models. Some extensions to nonlinear dynamic models are also considered.
Students will be given selected advanced research material for independent study and examination.
This module is available for students on a course where it is a listed option and as an Unusual Option to students who have completed the prerequisite modules.
Pre-requisites:
Statistics Students: ST218 Mathematical Statistics A AND ST219 Mathematical Statistics B
Non-Statistics Students: ST220 Introduction to Mathematical Statistics
Forecasting is a vital prerequisite to decision making. This course offers a very powerful fundamental probabilistic approach to forecasting, controlling and learning about uncertain commercial, financial, economic, production, environmental and medical dynamic systems. The theory will be illustrated by real examples from industry, marketing, finance, government, agriculture etc.
A familiarity with the material in this module will be very useful to all students planning a career involving a component of industrial, business or government statistics.
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.
By the end of the module, students should be able to:
View reading list on Talis Aspire
Demonstrate facility with rigorous statistical methods.
Evaluate, select and apply appropriate mathematical and/or statistical techniques.
Demonstrate knowledge of and facility with formal statistical concepts, both explicitly and by applying them to the solution of mathematical problems.
Create structured and coherent arguments communicating them in written form. 
Construct logical arguments with clear identification of assumptions and conclusions.
Reason critically, carefully, and logically.
Problem solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Know how to learn from failure.
Self awareness: Reflect on learning, seeking feedback on and evaluating personal practices, strengths and opportunities for personal growth.
Communication: Present arguments, knowledge and ideas, in a range of formats.
Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Manage priorities and time. Self-motivated, setting and achieving goals, prioritising tasks.
Type | Required | Optional |
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Lectures | 30 sessions of 1 hour (20%) | 2 sessions of 1 hour |
Private study | 120 hours (80%) | |
Total | 150 hours |
Study of advanced topic, weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Weighting | Study time | |
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On-campus Examination | 100% | |
The examination will contain one compulsory question on the advanced topic and four additional questions of which the best marks of TWO questions will be used to calculate your grade.
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Weighting | Study time | |
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On-campus Examination - Resit | 100% | |
The examination will contain one compulsory question on the advanced topic and four additional questions of which the best marks of TWO questions will be used to calculate your grade.
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Solutions and cohort level feedback will be provided for the examination.
If you take this module, you cannot also take:
This module is Optional for:
This module is Option list A for:
This module is Option list B for:
This module is Option list E for:
This module is Option list F for: