Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
| MATH1000 | Core Mathematics |
| MATH1700 | Probability and Statistics |
MATH3802 Time Series
This module is not approved as a discovery module
Time series are any data which is observed repeatedly over time, such as climate data, stock prices, or population numbers. In time series, measurements are made at a succession of times, and it is the dependence between measurements taken at different times which is important. The module will concentrate on techniques for model identification, parameter estimation, diagnostic checking and forecasting within the autoregressive moving average family of models and their extensions.
To develop statistical techniques for the analysis of data collected sequentially through time. Students will gain an appreciation of model fitting for time series data and also how models can be used for projections.
On successful completion of the module students will have demonstrated the following learning outcomes:
1. Assess graphically the stationarity of a time series, including the calculation and use of a sample autocorrelation on function;
2. Evaluate the autocorrelation function and partial autocorrelation function for AR, MA and ARMA modes;
3. Use the autocorrelation and partial autocorrelation functions and other diagnostics to formulate, test and modify suitable hypotheses about time series models;
4. Forecast future values of a time series;
5. Use statistical software for simulation and data analysis.
6. Analyse real time-series data and write a report giving and interpreting the results.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
a. Communicate information about time series data through written work and reasoning.
b. Use statistics packages to analyse time series data and conduct forecasting.
c. Understand important and critical concepts of time series modelling.
d. Use technology appropriately in your work and studies.
1. Stationarity, definition and identification.
2. Outline of Box-Jenkins approach through identification of model, removal of trend and seasonality, fitting, diagnostic checking, and forecasting.
3. Autocorrelation function, partial autocorrelation function.
4. AR, MA, ARMA, and ARIMA models, their autocorrelation functions, and partial autocorrelation functions. Transformations and differencing to achieve stationarity.
5. Estimation and diagnostics. Identifying possible models using autocorrelation function, and partial autocorrelation function. Estimation, outline of maximum likelihood, conditional and unconditional least squares approaches. Diagnostic checking, methods and suggestions of possible model modification.
6. Forecasting. Minimum mean square error forecast and forecast error variance, confidence intervals for forecasts, updating forecasts, other forecasting procedures.
Additional topics that build on these may be covered as time allows. Further details of possible topics will be delivered closer to the time that the module runs.
Methods of assessment
The assessment details for this module will be provided at the start of the academic year
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 11 | 2 | 22 |
| Practical | 4 | 1 | 4 |
| Seminar | 6 | 1 | 6 |
| Private study hours | 68 | ||
| Total Contact hours | 32 | ||
| Total hours (100hr per 10 credits) | 100 | ||
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team