STU33010 – Forecasting

Module CodeSTU33010
Module NameForecasting
ECTS Weighting[1] 5 ECTS
Semester taughtSemester 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.

Teaching and learning Methods

Lectures, online quizzes, online Discussion Board

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of totalWeek setWeek Due
Continuous assessmentProject
Analysis of time series
ExaminationReal-time exam (2hrs)LO1-460%

Reassessment Details

Examination, 2 hours, 100%

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:0 hours
Lecture33 hours
Laboratory0 hours
Tutorial or seminar0 hours
Other0 hours
Independent study (outside scheduled contact hours), broken down by:83 hours
Preparation for classes and review of material (including preparation for examination, if applicable40 hours
completion of assessments (including examination, if applicable)43 hours
Total Hours116 hours

Recommended Reading List

Forecasting: principles and practice by R. Hyndman and G. Athanasopoulos online book at

Module Pre-requisites

Prerequisite modules:

Other/alternative non-module prerequisites:

statistics (linear regression), R programming languages.

Module Co-requisites


Module Website


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