Introductory description

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; or,
  • ST228 Mathematical Methods for Statistics and Probability and ST229 Probability for Mathematical Statistic.

• Non-Statistics Students:

  • ST220/ST226 Introduction to Mathematical Statistics; or,
  • ST232/ST233 Introduction to Mathematical Statistics.

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.

• State space modelling
• Bayesian updating of beliefs
• Specifying Dynamic Linear Models
• Updating Dynamic Linear Models, forecasting
• Building Dynamic Linear Models, accommodating external information
• ARIMA models, stationarity
• Understand by independent study an additional advanced topic in Bayesian Forecasting & Intervention

Learning outcomes

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

• Acquire an appreciation of forecasting recurrences and be able to calculate these for special cases.
• Know how to select an appropriate model in simple scenarios
• Have an acquaintance with the most useful models in the class of DLMs for statistical models in a business environment.
• Know how to intervene in these processes in the light of external information
• Have an appreciation of diagnostics methods and estimation techniques for this model class.
• Understand how to deal with non-linearity in a model using sequential Monte Carlo techniques
• To understand by independent study selected advanced research material

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

• 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.

Transferable skills

• 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.