Module Details

Module Code: STAT9005
Title: Time Series & Factor Analysis
Long Title: Time Series & Multivariate Analysis
NFQ Level: Expert
Valid From: Semester 2 - 2024/25 ( January 2025 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 1 programme(s)
Module Description: This module will provide the learner with the necessary tools to develop and critically evaluate structural equation modelling and time series models. In this module, the dimensionality of the data will be reduced and understood using multivariate tools such as factor and principal components analyses, while the forecasting and prediction will be performed by different statistical methods on the field of the time series analysis. These techniques will enable 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 Apply the theoretical principles that govern a time series.
LO2 Apply regression and time series model for prediction. Differentiate between pure and causal time series models.
LO3 Critically analyse and report on the paradigm under which forecasts are being made, along with their reliability. Perform residuals analysis and tests of fit.
LO4 Implement multivariate tools such as factor analysis techniques on a large dataset and interpret the resulting models.
LO5 Use statistical packages to generate and analyse models.
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
Multivariate analysis
Assumptions for Dimensionality reduction techniques, Covariance matrix and Loading Factors, Data screening, Principal Component Analysis and Axes Rotations, Exploratory and Confirmatory Factor Analysis (EFA/CFA), Model Fit and Validadation, Structural Equation Modelling (SEM).
Time series analysis
Time Series Decomposition (trend and cyclical components, seasonality and remainder), Moving Average, Exponential Smoothing Techniques, Stationarity and Model transformation, Autoregressive (AR), Moving Average (MA), mixed integrated (ARIMA) and Seasonally Adjusted ARIMA (SARIMA) models. Forecasting and Model Comparison indicators, ACF/PACF for Model Selection.
Forecasting
Forecast Error, Confidence Intervals, MAE, MAPE, RMSE, Ljung-Box statistic.
Software packages
Excel, R / RStudio.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Open-book Examination % of Total Mark 35
Timing Week 7 Learning Outcomes 1,2,3,5
Assessment Description
Time Series analysis - MCQ theoretical/practical test and Practical evaluation by implementing time series models in a given dataset.
Assessment Type Open-book Examination % of Total Mark 25
Timing Week 11 Learning Outcomes 4,5
Assessment Description
Multivariate Analysis using Dimensionality Reduction techniques (PCA and FA) - MCQ / SAQ evaluation of practical/theoretical knowledge of PCA/FA modelling, applications and interpretation of results to real-world datasets.
Assessment Type Project % of Total Mark 30
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
Report on data analysis results in a given dataset implementing Time Series and/or Dimensionality Reduction methods.
Assessment Type Presentation % of Total Mark 10
Timing Sem End Learning Outcomes 6
Assessment Description
Present methods and findings of data analysis performed on a 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 Lecture 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 Lecture 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
  • Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci. (2015), Introduction to Time Series Analysis and Forecasting, John Wiley & Sons, [ISBN: 1118745116].
  • Niels J. Blunch. (2013), Introduction to Structural Equation Modeling Using IBM SPSS Statistics and Amos, 2. Sage Publications Ltd, [ISBN: 978-144624900].
Supplementary Book Resources
  • Bruce L. Bowerman, Richard T. O'Connell, Anne B. Koehler. (2005), Forecasting, time series, and regression: An Applied Approach, Thomson Brooks/Cole, Belmont, CA, [ISBN: 978-0534409777].
  • Randall E. Schumacker. (2016), A Beginner's Guide to Structural Equation Modeling, 4. Routledge, [ISBN: 1138811939].
  • Timothy A. Brown. (2015), Confirmatory Factor Analysis for Applied Research, 2. Guilford Press, [ISBN: 1462515363].
  • Rex B. Kline. (2015), Principles and Practice of Structural Equation Modeling, 4. Guilford Press, [ISBN: 1462523344].
  • Alvin C. Rencher, William F. Christensen. (2012), Methods of Multivariate Analysis, 3. [ISBN: 1118391675].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SDAAN_9 Master of Science in Data Science & Analytics 2 Mandatory