# Course Catalogue

Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

## Statistics 3T: Time Series STATS3018

• School: School of Mathematics and Statistics
• Credits: 10
• Level: Level 3 (SCQF level 9)
• Typically Offered: Semester 2
• Available to Visiting Students: Yes
• Available to Erasmus Students: Yes

### Short Description

This course provides an introduction to the statistical modelling of time series data. The course focuses on the three main areas; (i) modelling trends and seasonal patterns; (ii) modelling short-term correlation; and (iii) predicting observations at future points in time.

### Timetable

20 lectures

fortnightly tutorials

2 two-hour practical sessions

### Requirements of Entry

The normal requirement is that students should have been admitted to the third year of the Designated Degree programme in Statistics.

### Excluded Courses

Time Series [STATS4037]

Time Series (Level M) [STATS5030]

### Co-requisites

The courses prescribed in the Designated Degree programme to which the student has been admitted.

### Assessment

90 minute, end of course examination (100%)

Main Assessment In: April/May

### Course Aims

The aims of this course are:

■ to introduce the concept of a time series, and discuss a range of descriptive methods for identifying features of interest;

■ to present an overview of the approaches for representing trends and seasonality in a time series, and to assess their relative merits;

■ to describe a range of approaches for predicting future values of a time series;

■ to show how to apply the techniques from the course to real time series data sets in the statistical package R.

### Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ determine whether a time series exhibits any evidence of a trend, seasonality or short-term correlation;

■ define what it means for a time series to be stationary;

■ define the class of ARIMA probability models;

■ determine whether a particular model from the class of ARIMA models is stationary and invertible;

■ determine an appropriate model for a data set from the class of ARIMA models;

■ predict future values for a given time series;

### Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.