Module Details

Module Code: DATA C9005
Full Title: Time Series Analysis
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 5
Module Owner:: Rajesh Jaiswal
Departments: Unknown
Module Description: This module builds on fundamentals of linear algebra needed to comprehend various dimension reduction techniques, time series and auto-correlated responses. The module focuses on dimension reduction techniques such as ICA and PCA of time series data for prediction and signal extraction. Students will learn techniques to build various time series models for time series forecasting.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Investigate the role of linear algebra in Statistics
MLO2 Interpret and implement dimension reduction techniques using basis vectors
MLO3 Design and develop 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.
MLO4 Perform diagnostic analysis and forecasts for time series models
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 DkIT 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
 
Module Indicative Content
Linear Algebra
Matrix Algebra, Eigenvalues, Eigenvectors, Linear transformations
Basis Vectors and Data Projections
Dimension reduction - Principle Components Analysis, Independent Component Analysis, Common Factor Analysis - Non-negative Matrix Factorisation
Time series Analysis
Time and Frequency domain analysis. Decomposition, Smoothing Techniques, Stationarity, Autocorrelation, Correlograms, Autoregressive (AR), Moving Average (MA) and ARIMA models.
Forecasting
Forecast Error, Confidence Intervals, MAE, MAPE, MPE, RMSE, Ljung-Box Statistic
Module Assessment
Assessment Breakdown%
Course Work50.00%
Final Examination50.00%
Module Special Regulation
 

Assessments

Full Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 15
Marks Out Of 100 Pass Mark 40
Timing Week 4 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Assignment covering the role of linear algebra in Statistics and application of dimension reduction techniques
Assessment Type Continuous Assessment % of Total Mark 35
Marks Out Of 100 Pass Mark 40
Timing Week 10 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Data Project 2- A cross-module end of semester project where students will use regression and time series model for a data analytics problem and perform a diagnostic analysis and carry out informed predictions. Here, students will be encouraged to explore the benefits of distributed computing environment for efficient extraction and storage of the time series data.
No Project
No Practical
Final Examination
Assessment Type Formal Exam % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4
Duration in minutes 120
Assessment Description
End of module examination covering all the learning outcomes

Part Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 15
Marks Out Of 100 Pass Mark 40
Timing Week 4 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Assignment covering the role of linear algebra in Statistics and application of dimension reduction techniques
Assessment Type Continuous Assessment % of Total Mark 35
Marks Out Of 100 Pass Mark 40
Timing Week 10 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Data Project 2- A cross-module end of semester project where students will use regression and time series model for a data analytics problem and perform a diagnostic analysis and carry out informed predictions. Here, students will be encouraged to explore the benefits of distributed computing environment for efficient extraction and storage of the time series data.
No Project
No Practical
Final Examination
Assessment Type Formal Exam % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4
Duration in minutes 120
Assessment Description
End of Module Examination covering all the learning outcomes
Reassessment Requirement
A 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.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact 1-hour lecture to cover theory of time series analysis Every Week 1.00 1
Practical Contact 2-hour labs with integrated tutorials Every Week 2.00 2
Directed Reading Non Contact Lecture notes, books and online materials Every Week 1.00 1
Independent Study Non Contact Lecture notes, books and online materials Every Week 4.00 4
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
Workload: Part Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact 1-hour lecture to cover theory of time series analysis Every Week 1.00 1
Practical Contact 2-hour labs with integrated tutorials Every Week 2.00 2
Directed Reading Non Contact Lecture notes, books and online materials Every Week 1.00 1
Independent Study Non Contact Lecture notes, books and online materials Every Week 4.00 4
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Peter J. Brockwell, Richard A. Davis. (2016), Introduction to Time Series and Forecasting (Springer Texts in Statistics).
Supplementary Book Resources
  • Aileen Nielsen. (2019), Practical Time Series Analysis.
This module does not have any article/paper resources
Other Resources