MT4527 Time Series Analysis

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

10.00 am Mon (even weeks), Tue and Thu

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr R C Pinto Borges

Dr R C Pinto Borges
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Regina Bispo

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module provides an introduction to univariate linear times series models (ARIMA processes) and univariate non-linear times-series models (ARCH and GARCH). The syllabus includes: forecasting methods for constant mean and trend models, the ARIMA class of models (including seasonal ARIMA models), fitting and forecasting ARIMA models, ARCH and GARCH processes.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT2508

Assessment pattern

2-hour Written Examination = 100%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks) and 0.5 tutorial (x 10 weeks).

Intended learning outcomes

  • Understand basic concepts related to Time Series modelling such as `white noise?, `stationarity?, and the autocorrelation function
  • Estimate the trend and seasonality within a data set, and perform forecasting using basic Time Series models such as the Constant Mean model and the Random Walk model
  • Fit to the data more complex models such the Moving Average and Autoregressive processes, as well as the more encompassing ARMA and ARIMA models
  • Use R packages to utilise ARMA and ARIMA modelling to forecast future observations, evaluate the associate uncertainty, and perform model comparison
  • Fit ARCH and GARCH models to estimate volatility and forecast future observations for asset return data