# Untitled
**Source**: https://teaching.scss.tcd.ie/wp-json/wp/v2/module/364
**Parent**: https://teaching.scss.tcd.ie/module/stu33010-forecasting/
{"id":364,"date":"2020-07-17T14:59:08","date\_gmt":"2020-07-17T14:59:08","guid":{"rendered":"https:\/\/teaching.scss.tcd.ie\/?post\_type=module&p=364"},"modified":"2026-03-11T12:03:03","modified\_gmt":"2026-03-11T12:03:03","slug":"stu33010-forecasting","status":"publish","type":"module","link":"https:\/\/teaching.scss.tcd.ie\/module\/stu33010-forecasting\/","title":{"rendered":"STU33010 – Forecasting"},"content":{"rendered":"\n
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| **Module Code<\/strong><\/td> STU33010<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Name<\/strong><\/td> Forecasting<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **ECTS Weighting[**[1]<\/strong><\/a><\/strong><\/td> 5 ECTS<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Semester taught<\/strong><\/td> Semester 2<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Coordinator\/s <\/strong><\/td> Alessio Benavoli<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Academic Year <\/strong><\/td> 2026\/2027<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nModule Learning Outcomes<\/h2>\n\n\n\n On successful completion of this module, students will be able to: <\/p>\n\n\n\n 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. <\/p>\n\n\n\n LO2: Program, analyse and select the best model for forecasting. <\/p>\n\n\n\n LO3: Interpret output of data analysis performed by a computer statistics package. <\/p>\n\n\n\n LO4: Compute predictions with their confidence intervals using the selected model.<\/p>\n\n\n\n <\/p>\n\n\n\nModule Content<\/h2>\n\n\n\n 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.<\/p>\n\n\n\nTeaching and learning Methods<\/h2>\n\n\n\n Lectures, group work;<\/p>\n\n\n\nAssessment Details<\/h2>\n\n\n\n <\/p>\n\n\n\n | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Assessment Component<\/strong><\/td> **Brief Description<\/strong><\/td> **Learning Outcomes Addressed<\/strong><\/td> **% of total<\/strong><\/td> **Week set<\/strong><\/td> **Week Due<\/strong><\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Continuous assessment<\/td> quizzes, project Analysis of time series<\/td> LO1-4<\/td> 30%<\/td> <\/td> <\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Examination<\/td> In-person exam (2 hours)<\/td> LO1-4<\/td> 70%<\/td> <\/td> <\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | \ufeff<\/td> \ufeff<\/td> \ufeff<\/td> \ufeff<\/td> \ufeff<\/td> \ufeff<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | \ufeff<\/td> \ufeff<\/td> \ufeff<\/td> \ufeff<\/td> \ufeff<\/td> \ufeff<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nReassessment Details<\/h2>\n\n\n\n In-person exam, 2 hours, 100%<\/p>\n\n\n\nContact Hours and Indicative Student Workload<\/h2>\n\n\n\n | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Contact Hours (scheduled hours per student over full module), broken down by<\/strong>:<\/td> **0 hours<\/strong><\/td><\/tr>| | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Lecture<\/td> 33 hours<\/td><\/tr>| | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Laboratory<\/td> 0 hours<\/td><\/tr>| | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Tutorial or seminar<\/td> 0 hours<\/td><\/tr>| | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Other<\/td> 0 hours<\/td><\/tr>| | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Independent study (outside scheduled contact hours), broken down by:<\/strong><\/td> **83 hours<\/strong><\/td><\/tr>| | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | Preparation for classes and review of material (including preparation for examination, if applicable<\/td> 40 hours<\/td><\/tr>| | | | | | | | --- | --- | --- | --- | --- | --- | | completion of assessments (including examination, if applicable)<\/td> 43 hours<\/td><\/tr>| | | | | | --- | --- | --- | --- | | **Total Hours<\/strong><\/td> **116 hours<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nRecommended Reading List<\/h2>\n\n\n\n <\/p>\n\n\n\n | | | | --- | --- | | Hyndman and G. Athanasopoulos *Forecasting: principles and practice by R <\/em>online book at [https:\/\/otexts.com\/fpp3\/<\/a> Harvey, Andrew C. *Forecasting, structural time series models and the Kalman filter<\/em>. Cambridge University Press 2014. Durbin, James, and Siem Jan Koopman. *Time series analysis by state space methods<\/em>. Vol. 38. OUP Oxford, 2012.<\/td><\/tr>| <\/th><\/tr><\/tbody><\/table><\/figure>\n\n\n\nModule Pre-requisites<\/h2>\n\n\n\n **Prerequisite modules:<\/strong> either (STU12501 and STU12502) or STU23501<\/p>\n\n\n\n **Other\/alternative non-module prerequisites:<\/strong> <\/p>\n\n\n\n Probability theory, statistics (linear regression, hypothesis testing), R programming language.<\/p>\n\n\n\nModule Co-requisites<\/h2>\n\n\n\n None<\/p>\n\n\n\nModule Website<\/h2>\n\n\n\n [Blackboard<\/a><\/p>\n\n\n\n [Collaborate Ultra (TCD only)<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" (Semester 1, 5 ECTS) Introduction to Forecasting; ARIMA models, data transformations, seasonality, exponential smoothing and Holt Winters algorithms, performance measures. 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