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STAT8008 - Time Series & PCA

Title:Time Series & PCA
Long Title:Time Series & PCA
Module Code:STAT8008
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 2 programme(s)
Next Review Date: March 2023
Module Coordinator: David Goulding
Module Author: Justin McGuinness
Module Description: This module introduces learners to the concepts of data dimension reduction and principle component analysis. Furthermore, it provides the learner with the necessary tools to develop and critically evaluate time series models. The forecasting function of time series models is presented and evaluated, enabling the learner to create short and medium term forecasting models.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Perform PCA to reduce dimensionality of datasets.
LO2 Describe the assumptions underlying PCA & time series models.
LO3 Apply the theoretical principles that govern a time series.
LO4 Apply regression and time series models for prediction, and give an account of the paradigm under which the forecasts are being made, along with their reliability.
LO5 Perform diagnostic analysis and forecasts for both PCA and time series models, using statistical software.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.

No requirements listed
No Co Requisites listed

Module Content & Assessment

Indicative Content
Principle Component Analysis
PC eigenvalues & eigenvectors, Scree plots, PC Loadings & Scores, Goodness of fit of PC models, Regression and prediction using PCs, Rotations, KMO & Bartlet's test of sphericity
Time series analysis
Decomposition (trend, periodicity, seasonality, white noise), Smoothing Techniques, Stationarity, Autocorrelation, Correlograms, Autoregressive (AR), Moving Average (MA) and mixed (ARIMA) models, R-Square, Stationary R-Square, BIC
Forecast Error, Confidence Intervals, MAE, MAPE, MPE, RMSE, Ljung-Box Statistic
Software analysis
R, Minitab, SPSS
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Practical/Skills Evaluation Apply PCA to a real-world data set and perform a critical analysis of the results 1,2,5 30.0 Week 5
Short Answer Questions Examination on time series analysis 1,3,4 20.0 Week 9
Practical/Skills Evaluation Solve and analyse time series data sets and report on the results 1,3,4,5 40.0 Sem End
Presentation Present findings of time series analysis of a data set. 1,3,4,5 10.0 Sem End
No End of Module Formal Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute reserves the right to alter the nature and timings of assessment


Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 2.0 Every Week 2.00
Lab Computer practical 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Work based on texts and class material 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 1.5 Every Week 1.50
Lab Computer practical 1.5 Every Week 1.50
Independent & Directed Learning (Non-contact) Work based on texts and class material 4.0 Every Week 4.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00

Module Resources

Recommended Book Resources
  • G. James, D. Witten, T. Hastie, R. Tibshirani 2013, An Introduction to Statistical Learning with Applications in R, 4th Edition Ed., Springer [ISBN: 9781461471370]
  • Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci 2015, Introduction to Time Series Analysis and Forecasting, 2nd Edition Ed., John Wiley & Sons [ISBN: 9781118745113]
Supplementary Book Resources
  • Alvin C. Rencher, William F. Christensen 2012, Methods of Multivariate Analysis, 3rd Edition Ed., John Wiley & Sons [ISBN: 9781118391686]
  • I.T. Jolliffe 2002, Principal Component Analysis, 2nd Edition Ed., Springer-Verlag New York [ISBN: 9780387954424]
  • Bruce L. Bowerman, Richard T. O'Connell, Anne B. Koehler 2005, Forecasting, time series, and regression: An Applied Approach, 4th Edition Ed., Thomson Brooks/Cole Belmont, CA [ISBN: 978-053440977]
This module does not have any article/paper resources
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
CR_BBISY_8 Bachelor of Business (Honours) in Information Systems 8 Elective
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Elective

Cork Institute of Technology
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Email: help@cit.edu.ie