Module Details
Module Code: |
DATA C9005 |
Full Title:
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Time Series Analysis
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Valid From:: |
Semester 1 - 2019/20 ( June 2019 ) |
Language of Instruction: | English |
Module Owner:: |
Rajesh Jaiswal
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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.
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Module Learning Outcome |
On successful completion of this module the learner will be able to: |
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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).
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No recommendations listed |
Module Indicative Content |
Linear Algebra
Matrix Algebra, Eigenvalues, Eigenvectors, Linear transformations
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Basis Vectors and Data Projections
Dimension reduction - Principle Components Analysis, Independent Component Analysis,
Common Factor Analysis - Non-negative Matrix Factorisation
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Time series Analysis
Time and Frequency domain analysis. Decomposition, Smoothing Techniques, Stationarity, Autocorrelation, Correlograms, Autoregressive (AR), Moving Average (MA) and ARIMA models.
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Forecasting
Forecast Error, Confidence Intervals, MAE, MAPE, MPE, RMSE, Ljung-Box Statistic
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Module Assessment
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Assessment Breakdown | % |
Course Work | 50.00% |
Final Examination | 50.00% |
Module Special Regulation |
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AssessmentsFull Time On Campus
Part Time On Campus
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.
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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
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Recommended Book Resources |
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Peter J. Brockwell, Richard A. Davis. (2016), Introduction to Time Series and Forecasting (Springer Texts in Statistics).
| Supplementary Book Resources |
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Aileen Nielsen. (2019), Practical Time Series Analysis.
| This module does not have any article/paper resources |
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Other Resources |
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Website, GITHUB - python,
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