Module Details

Module Code: STAT8008
Title: Time Series & PCA
Long Title: Time Series & Multivariate Analysis
NFQ Level: Advanced
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 2 programme(s)
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:
# Learning Outcome Description
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.
LO6 Present the analysis techniques, findings and results using appropriate presentation and visual communication skills.
Dependencies
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 MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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
Requirements

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
 
Indicative Content
Dimensionality Reduction Techniques
Assumptions for dimensionality reduction, Covariance and Correlation, 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, PCA methods in the Factor Analysis models.
Time series analysis
Decomposition (trend and cycles, seasonality and remainder components), Classical Methods for TS analysis, Stationarity, Correlograms, Autoregressive (AR), Moving Average (MA) and mixed (ARIMA) and Seasonally Adjusted ARIMA (SARIMA) models, Model Selection.
Forecasting
Forecast Error, Confidence Intervals, MAE, MAPE, MPE, RMSE, Ljung-Box Statistic
Software analysis
R & RStudio, Excel
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Open-book Examination % of Total Mark 30
Timing Week 7 Learning Outcomes 2,3,4,5
Assessment Description
Time series analysis Theoretical and Practical evaluation - MCQ test and time series analysis performing on a given dataset.
Assessment Type Open-book Examination % of Total Mark 30
Timing Week 11 Learning Outcomes 1,2,5
Assessment Description
PCA theory test and practical evaluation - MCQ test and PCA to a real-world data with critical analysis of the results.
Assessment Type Project % of Total Mark 30
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
Analyse some data sets using Time Series and Dimensionality reduction techniques, and report on the results
Assessment Type Presentation % of Total Mark 10
Timing Sem End Learning Outcomes 6
Assessment Description
Present methods and findings of the analysis on a given data set.
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 University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module Content delivery Every Week 2.00 2
Lab Contact Computer practical Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Work based on texts and class material Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module Content delivery Every Week 1.50 1.5
Lab Contact Computer practical Every Week 1.50 1.5
Independent & Directed Learning (Non-contact) Non Contact Work based on texts and class material Every Week 4.00 4
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. Springer, [ISBN: 9781461471370].
  • Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci. (2015), Introduction to Time Series Analysis and Forecasting, 2nd Edition. John Wiley & Sons, [ISBN: 9781118745113].
Supplementary Book Resources
  • Alvin C. Rencher, William F. Christensen. (2012), Methods of Multivariate Analysis, 3rd Edition. John Wiley & Sons, [ISBN: 9781118391686].
  • I.T. Jolliffe. (2002), Principal Component Analysis, 2nd Edition. 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. 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