CloseHelpPrint
Kies de Nederlandse taal
Course module: 2DD23
2DD23
Time series analysis and forecasting
Course info
Course module2DD23
Credits (ECTS)5
Category-
Course typeGraduate School
Language of instructionEnglish
Offered byEindhoven University of Technology; Mathematics and Computer Science; Mathematics;
Is part of
Computer Science and Engineering
Data Science in Engineering (CSE)
Industrial and Applied Mathematics
Contact persondr. J.J.M. Rijpkema
Telephone3170
E-mailj.j.m.rijpkema@tue.nl
Lecturer(s)
Responsible lecturer
dr. J.J.M. Rijpkema
Feedback and reachability
Other course modules lecturer
Contactperson for the course
dr. J.J.M. Rijpkema
Other course modules lecturer
Academic year2016
Period
4  (24/04/2017 to 09/07/2017)
Starting block
4
TimeslotA1: A1 - Mo 1-2, Th 5-6
Course mode
Fulltime
Remarks-
Registration openfrom 15/06/2016 up to and including 26/03/2017
Application procedureYou apply via OSIRIS Student
Explanation-
Registration using OSIRISYes
Registration open for students from other department(s)Yes
Pre-registrationNo
Waiting listNo
Number of insufficient tests-
Number of groups of preference0
Learning objectives
After successful completion of the course participants should have gained insight and experience with current approaches for time series analysis, modeling and forecasting. More specifically this holds for exponential smoothing models (Simple, Holt and Holt-Winter), for Box-Jenkins models (ARMA, ARIMA, SARIMA) and for multivariate time series models (transfer function and XARIMA-models). Furthermore, participants should be able to analyze, model and validate time series data with representative software, like SPSS or R, independently and use the models obtained for time series forecasting and scenario analysis.
Content
Time series occur in a wide range of disciplines, ranging from business, economics and social sciences to biomedical and engineering contexts. In analyzing time series one searches for structures and patterns to describe and explain the underlying process and to forecast, based on adequate models fitted, future values or to predict results from alternative scenarios. In the course 2DD23, "Time series analysis and forecasting", apart from the "traditional" methods for trend and seasonal decomposition of time series (eg. Holt-Winter exponential smoothing models), more advanced statistical techniques available for these tasks, both in the time-domain (eg.Box-Jenkins ARMA-models) and in the frequency domain (eg. spectral and periodogram analyses) are discussed and underlying principles are explained. Furthermore attention is paid to the analysis of multivariate time series that are cross-correlated (Transfer function models and XARIMA models). The use of representative statistical software like SPSS or R is demonstrated and participants get the opportunity for hands-on experience using SPSS or R for time series analysis and forecasting.
Entrance requirements
Entrance requirements tests
-
Assumed previous knowledge
2DL20 Statistics
2DD80 Statistics
Previous knowledge can be gained by
-
Resources for self study
-
Short promotional description of the course
Can we predict the future from the past? An important question in a wide range of disciplines, ranging from business, operations, planning and control to biomedical and engineering contexts! This is where Time Series Analysis and Forecasting comes in, introducing an efficient statistical approach for modeling and analyzing time-dependent data, understanding the generating process and forecasting future behavior!
Short promotional description of the course
Can we predict the future from the past? An important question in a wide range of disciplines, ranging from business, operations, planning and control to biomedical and engineering contexts! This is where Time Series Analysis and Forecasting comes in, introducing an efficient statistical approach for modeling and analyzing time-dependent data, understanding the generating process and forecasting future behavior!
Required materials
-
Recommended materials
Available as e-book from https://www.otexts.org/fpp
ISBN:978-0987507105
Title:Forecasting: principles and practice Paperback
Author:Hyndman, Rob. J. et al
Available as e-book through Vubis.
ISBN:978-0387886978
Title:Introductory Time Series with R
Author:Andrew V. Metcalfe and Paul S.P. Cowpertwait
Publisher:Springer 2009
Statistical Software R
https://www.r-project.org/ (R)
Instructional modes
College / course

General
-

Remark
-
Tutorial

General
-

Remark
-
Tests
Assignment(s)
Test weight100
Minimum grade6
Test typeAssignment(s)
Number of opportunities1
OpportunitiesBlock 4
Test duration in minutes-

Assessment
-

Remark
-

Oral examination
Test weight0
Minimum grade6
Test typeOral examination
Number of opportunities1
OpportunitiesBlock 4
Test duration in minutes-

Assessment
-

Remark
-

CloseHelpPrint
Kies de Nederlandse taal