Introductory description
This module runs in Term 2 and is concerned with the theory and practice of shortterm 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.
Prerequisites:
Statistics Students: ST218 Mathematical Statistics A AND ST219 Mathematical Statistics B
NonStatistics Students: ST220 Introduction to Mathematical Statistics
Module web page
Module aims
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.
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 nonlinearity 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
TBC
Transferable skills
TBC
Study time
Type 
Required 
Optional 
Lectures 
30 sessions of 1 hour (20%)

2 sessions of 1 hour

Private study 
120 hours (80%)


Total 
150 hours 

Private study description
Study of advanced topic, weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
Costs
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.
Assessment group B3

Weighting 
Study time 
Inperson 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.
 Answerbook Pink (12 page)
 Cambridge Statistical Tables (blue)

Assessment group R1

Weighting 
Study time 
Inperson 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.
~Platforms  Moodle
 Answerbook Pink (12 page)
 Students may use a calculator
 Cambridge Statistical Tables (blue)

Feedback on assessment
Solutions and cohort level feedback will be provided for the examination.
Past exam papers for ST405
Courses
This module is Optional for:

TMAAG1PE Master of Advanced Study in Mathematical Sciences

Year 1 of
G1PE Master of Advanced Study in Mathematical Sciences

Year 1 of
G1PE Master of Advanced Study in Mathematical Sciences

Year 1 of
TIBSN3G1 Postgraduate Taught Financial Mathematics

Year 1 of
TMAAG1PD Postgraduate Taught Interdisciplinary Mathematics (Diploma plus MSc)

Year 1 of
TMAAG1P0 Postgraduate Taught Mathematics

Year 1 of
TMAAG1PC Postgraduate Taught Mathematics (Diploma plus MSc)

Year 1 of
TMAAG1PF Postgraduate Taught Mathematics of Systems

Year 1 of
TSTAG4P1 Postgraduate Taught Statistics

USTAG300 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 A for:

Year 4 of
USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics

Year 5 of
USTAG301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated

USTAG1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)

Year 3 of
G1G3 Mathematics and Statistics (BSc MMathStat)

Year 4 of
G1G3 Mathematics and Statistics (BSc MMathStat)

USTAG1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)

Year 4 of
G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)

Year 5 of
G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
This module is Option list B for:

Year 4 of
USTAG304 Undergraduate Data Science (MSci)

Year 4 of
UCSAG4G3 Undergraduate Discrete Mathematics

Year 3 of
USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics

USTAG301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated

Year 3 of
G30E Master of Maths, Op.Res, Stats & Economics (Actuarial and Financial Mathematics Stream) Int

Year 4 of
G30E Master of Maths, Op.Res, Stats & Economics (Actuarial and Financial Mathematics Stream) Int
This module is Option list E for:

Year 4 of
USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics

Year 5 of
USTAG301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated
This module is Option list F for:

Year 3 of
USTAG300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics

USTAG301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated

Year 3 of
G30H Master of Maths, Op.Res, Stats & Economics (Statistics with Mathematics Stream)

Year 4 of
G30H Master of Maths, Op.Res, Stats & Economics (Statistics with Mathematics Stream)