# STU33010 – Forecasting

**Source**: https://teaching.scss.tcd.ie/module/stu33010-forecasting/
**Parent**: https://www.maths.tcd.ie/undergraduate/modules/minor-stats.php

|  |  |
| --- | --- |
| **Module Code** | STU33010 |
| **Module Name** | Forecasting |
| **ECTS Weighting[**[1]**](https://teaching.scss.tcd.ie/wp-admin/post.php?post=375&action=edit#_ftn1)** | 5 ECTS |
| **Semester taught** | Semester 2 |
| **Module Coordinator/s** | Alessio Benavoli |
| **Academic Year** | 2026/2027 |

## 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

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| **Assessment Component** | **Brief Description** | **Learning Outcomes Addressed** | **% of total** | **Week set** | **Week Due** |
| Continuous assessment | quizzes, project Analysis of time series | LO1-4 | 30% |  |  |
| Examination | In-person exam (2 hours) | LO1-4 | 70% |  |  |
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## 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

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| --- |
| 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. |
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## 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

## Module Website

[Blackboard](https://tcd.blackboard.com/webapps/login/)

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