Postgraduate Course: Time Series Forecasting (CMSE11640)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Time Series Forecasting - the actionable mathematical framework that equips aspiring Business Analysts and Data Scientists with the toolkit to perform predictions based on classic models that have stood the test of time as well as elementary Machine Learning models that can tap into the potential of big and nonlinear datasets. In this course the student will be initiated to methods and tactics on how to perform necessary time-series pre-processing; statistically sound model fitting; short-term and long-term forecasting; as well as constructing brief presentations with meaningful visualizations that can be communicated to non-experts. Finally, they will be assessed by drafting a report in which they will demonstrate their combined knowledge, on a practical business case. |
| Course description |
This course aim is to provide the time series forecasting for Business Analytics. Business Analytics is a discipline that sits at the boundary of the Social Sciences and of Science & Engineering. Time series analysis in general is a subfield of business analytics which deals with data that change over time. Time series forecasting especially is the branch of time series analysis which deals with mathematical methods designed to forecast future values of the data that change over time. Therefore, by enabling the quantitative prediction of the future values of datasets, time series forecasting is core to Business Analytics. It is then essential that Business Analysts & Data Scientists familiarise with methods, tools, and tactics regrading forecasting of time series data. The objective of this course is then to investigate the elements and practice of time series forecasting.
Topics that will be covered may include:
-clean and pre-process time series datasets;
-time series forecasting with classic models;
-time series forecasting with machine learning models;
-forecast accuracy metrics and visualizations;
-report building and presentation.
Tutorial/seminar hours represent the minimum total live hours - online - a student can expect to receive on this course. These hours may be delivered in tutorial/seminar, workshop or other interactive whole class or small group format. These live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously. Live sessions will be delivered only once.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Applied Machine Learning (CMSE11614)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Optional course for Online MSc in Data and Decision Analytics. |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
86 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessment within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Clean and pre-process time series data.
- Fit time series data and perform forecasts with classic models.
- Fit time series data and perform forecasts with machine learning models.
- Estimate forecast accuracy metrics and develop meaningful visualisations.
- Build reports and present their results.
|
Reading List
Shmueli, Galit; Lichtendahl. Practical Time Series Forecasting with R: A Hands-On Guide. 2nd ed. Green Cove Springs, USA: Axelrod Schnall Publishers, 2016. Print.
Lazzeri, Francesca. Machine Learning for Time Series Forecasting with Python / Francesca Lazzeri. Indianapolis: Wiley, 2021. Print.
Kirchgässner, Gebhard, and Jürgen Wolters. Introduction to Modern Time Series Analysis. 1. Aufl. Berlin u.a: Springer-Verlag, 2007. Web. |
Additional Information
| Graduate Attributes and Skills |
After completing this course, students should be able to:
Communication, ICT, and Numeracy Skills
-Critically evaluate and present digital and other sources, research methods, data and information; discern
their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of
organisational contexts.
Cognitive Skills
-Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating
adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to
demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to
quality
Knowledge and Understanding
-Identify, define and analyse theoretical and applied business and management problems, and develop
approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore
and solve them responsibly.
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| Keywords | Time series,forecasting |
Contacts
| Course organiser | Dr Stavros Stavroglou
Tel: (0131 6)51 1603
Email: Stavros.Stavroglou@ed.ac.uk |
Course secretary | |
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