The University of Southampton
Courses

# STAT6087 Time Series Analysis

## Module Overview

This module will provide an introduction to time series models in common use and their use for predicting future observations and/or estimating unobservable components like trend and seasonal effects.

### Aims and Objectives

#### Module Aims

To introduce students to time series models in common use and their use for predicting future observations and/or estimating unobservable components like trend and seasonal effects.

#### Learning Outcomes

##### Learning Outcomes

Having successfully completed this module you will be able to:

• Understand and be able to apply the concepts and methods underlying the analysis of univariate time series, and the context for interpretation of results
• Decompose a time series into trend, seasonal and irregular components
• Understand the theoretical bases of different methods of time series analysis including decomposition
• Determine how and when to apply different methods of time series analysis and how to test for goodness of fit using the software package X12

### Syllabus

• Difference of time series data compared to other data sets (equidistant observations, calendar effects, outliers) • Basic concepts of time series: Stationarity, Ergodicity, Autocorrelations, Partial Autocorrelations • Global models for trends and seasonals • The periodogram and spectral analysis • Local models and moving average methods • ARIMA modelling and forecasting • Exponential smoothing • Estimation of unobservable components using a software package (X12ARIMA)

#### Special Features

Practical lab session in X12ARIMA software taught by ONS experts. This module is run as a week-long short course, a component of the MSc in Official Statistics

### Learning and Teaching

TypeHours
Independent Study66
Teaching34
Total study time100

Chatfield, C. (1996). The Analysis of Time Series: An Introduction.

Harvey, A.C. (1993). Time Series Models.

Laboratory space and equipment required. Practical computing lab in X12ARIMA

Harvey, A.C. (1989). Forecasting Structural Time Series Models and the Kalman Filter.

Wei, W. S. (1994). Time Series Analysis: Univariate and Multivariate Methods.

### Assessment

#### Summative

MethodPercentage contribution
Coursework 100%

#### Referral

MethodPercentage contribution
Coursework assignment(s) 100%

#### Repeat Information

Repeat type: Internal & External

#### Pre-requisites

To study this module, you will need to have studied the following module(s):

CodeModule
STAT6095Regression Modelling

### Costs

#### Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

##### Approved Calculators

Candidates may use calculators in the examination room only as specified by the University and as permitted by the rubric of individual examination papers. The University approved model is Casio FX-570 This may be purchased from any source and no longer needs to carry the University logo.

##### Stationery

You will be expected to provide your own day-to-day stationery items, e.g. pens, pencils, notebooks, etc.

##### Textbooks

Where a module specifies core texts these should generally be available on the reserve list in the library. However due to demand, students may prefer to buy their own copies. These can be purchased from any source. Some modules suggest reading texts as optional background reading. The library may hold copies of such texts, or alternatively you may wish to purchase your own copies. Although not essential reading, you may benefit from the additional reading materials for the module.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.