MS4H04 - Time Series and Forecasting 04 Dec 2020 - 31 Aug 2026 | Version 1
Associated Module Information
| Module Code: | MS4H04 | ||
|---|---|---|---|
| Module Title: | Time Series and Forecasting | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Maths | ||
| Module Leader: | Angelica Pachon | ||
| Module Team: | Stephanie Perkins, Rebecca Peters | ||
| First Intended Intake: | JAN 2025 | Final Year of Intake: | 2025 |
| Date Closed: | |||
| Credit Value: | 10 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100992 - machine learning | 101034 - statistical modelling | |
| HECOS Code Weighting: | 25 | 75 | |
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 04 Dec 2020 |
| Valid To | 31 Aug 2026 |
Module Aims
To provide an understanding into the discovery, interpretation, and communication of meaningful patterns in data and the ability to implement, interpret and critically analyse results from complex models.
Content Summary
This module aims to explore time series modelling including classical additive and multiplicative models. A detailed presentation of trend and seasonal decomposition, for a range of scenarios, will constitute the core of the module. As a result, students will develop strong foundations to understand independently more advanced models.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 8 |
| Seminar | 8 |
| Tutorial | 8 |
| Independent Study | 40 |
| Directed Study | 36 |
| Total Hours Selected | 100 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Understand underlying concepts and theory of analytical techniques through industry software. |
| LO2 | Critically analyse, interpret, and evaluate the outputs of time series & forecasting techniques to support useful insights from complex datasets. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Set Exercise - Not Time Constrained (CW) | Set Tasks - not-time constrained 1 | Analyse a data source using suitable statistical modelling techniques and report appropriate conclusions from the results. | 0 | 3000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Set Tasks - not-time constrained 1 | ✔ | ✔ | |