| Module Code | STU33010 |
| Module Name | Forecasting |
| ECTS Weighting[1] | 5 ECTS |
| Semester taught | Semester 1 |
| Module Coordinator/s | Alessio Benavoli |
Module Learning Outcomes
On successful completion of this module, students will be able to:
LO1: Define and describe the different patterns that can be found in times series and propose algorithms and statistical models that are suitable for their analysis.
LO2: Program, analyse and select the best model for forecasting.
LO3: Interpret output of data analysis performed by a computer statistics package.
LO4: Compute predictions with their confidence intervals using the selected model.
Module Content
Introduction to Forecasting; ARIMA models, data transformations, seasonality, exponential smoothing and Holt Winters algorithms, performance measures. Use of transformations and differences. Global models. Linear additive models. Kalman filtering.
Teaching and learning Methods
Lectures, group work;
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of total | Week set | Week Due |
| Continuous assessment | Project Analysis of time series | LO1-4 | 30% | 8 | 11 |
| Examination | In-person exam (2 hours) | LO1-4 | 70% | ||
| | | | | | |
| | | | | | |
Reassessment Details
In-person exam, 2 hours, 100%
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 0 hours |
| Lecture | 33 hours |
| Laboratory | 0 hours |
| Tutorial or seminar | 0 hours |
| Other | 0 hours |
| Independent study (outside scheduled contact hours), broken down by: | 83 hours |
| Preparation for classes and review of material (including preparation for examination, if applicable | 40 hours |
| completion of assessments (including examination, if applicable) | 43 hours |
| Total Hours | 116 hours |
Recommended Reading List
| Hyndman and G. Athanasopoulos Forecasting: principles and practice by R online book at https://otexts.com/fpp3/ Harvey, Andrew C. Forecasting, structural time series models and the Kalman filter. Cambridge University Press 2014. Durbin, James, and Siem Jan Koopman. Time series analysis by state space methods. Vol. 38. OUP Oxford, 2012. |
Module Pre-requisites
Prerequisite modules: either (STU12501 and STU12502) or STU23501
Other/alternative non-module prerequisites:
Probability theory, statistics (linear regression, hypothesis testing), R programming language.
Module Co-requisites
None