(Last Updated:03 May 2017)

**Total Credits: **20

**Level: **Level 4

**Target Students: **Single Honours students, and students taking MSc in Statistics and MSc in Statistics and Applied Probablity in the School of Mathematical Sciences. * Available to JYA/Erasmus students.*

**Taught Semesters:**

Semester | Assessment |
---|---|

Spring | Assessed by end of Spring Semester |

**Prerequisites: **G14FOS is required as a pre-requisite for MSc students only instead of G12SMM

Mnem | Title |
---|---|

G12SMM | Statistical Models and Methods |

**Corequisites: **None.

**Summary of Content: **This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real time series data will be described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. Students will gain experience of using a statistical package and interpreting its output. The module will cover:

- concepts of stationary and non-stationary time-series;
- philosophy of model building in the context of time series analysis;
- simple time series models and their properties;
- the model identification process;
- estimation of parameters;
- assessing the goodness of fit;
- methods for forecasting;
- use of a statistical package.

**Method and Frequency of Class: **

Activity | Number Of Weeks | Number of sessions | Duration of a session |
---|---|---|---|

Lecture | 10 weeks | 1 per week | 1 hour |

Lecture | 10 weeks | 1 per week | 2 hours |

Workshop | 10 weeks | 1 per week | 1 hour |

Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester

One two-hour lecture and one one-hour lecture per week timetabled centrally, some of which may be used for examples classes, problem classes and/or computer labs.

**Method of Assessment: **

Assessment Type | Weight | Requirements |
---|---|---|

Exam 1 | 80 | 2 hour 30 minute written examination |

Project 1 | 20 | Individual investigation using a computer package |

**Convenor: **

Professor A Wood

**Education Aims: **The purpose of this module is to deepen and broaden the students’ knowledge and experience of statistics by studying the theory and methods used in time series and forecasting.

This module is in the Statistics Pathway and builds upon the statistical ideas and methods and probability techniques introduced in the modules G12SMM and G12PMM or in the module G14FOS. Students will acquire knowledge and skills of relevance to a professional and/or research statistician.

**Learning Outcomes: **

A student who completes this module successfully will be able:

L1 - to state and prove standard results in the time domain relating to the
theory, models and methods of time series and forecasting, and apply them
to examples;

L2 - to derive, calculate and explain properties of time domain models and
methods;

L3 - to derive appropriate point and interval estimators, and construct
suitable test procedures;

L4 - to use statistical softaware packages to
fit models to data sets, assess their fit, make predicitions, and
identify models underlying data sets

L5 - to analyse and explain statistical results in the context of time series
and forecasting.

L6 - to present a systematic account of concepts for time series
and forecasting

L7 - research and synthesize a topic related to forecasting
and time series.

**Offering School: **Mathematical Sciences

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