Time Series Analysis

This module introduces the analysis of time series, i.e. a series of observations observed at discrete points in time. The module considers both theoretical and applied aspects of time series. Modern techniques such as the Kalman filter will be explained. In addition, links to computational statistics will be made. For example, the Gibbs sampler will be analysed as a Markov model. Issues of predictability and analysis of goodness-of-fit will be highlighted.

Lectures will cover both the theoretical aspects and the practical applications using statistical software. You will be given problem sheets with both analytical exercises and numerical problems to be solved using the statistical software. You will be able to measure your progress using these exercises and have the opportunity to receive feedback in the tutorials. Full solutions will be provided subsequent to the tutorial (including any code).

The coursework will comprise of an individual data analysis project to be carried out using the tools covered in the module lectures and tutorials. Problems sets will prepare you in using the necessary statistical software and in the presentation of the results.

At the end of this module you will be able to:

• Describe the structure and the purpose of time series models and assess the suitability of different models in reference to the theory of time series analysis
• Analyse and appraise the properties of particular models, e.g. ARMA(1,1) models
• Demonstrate expertise in the application of statistical software to analyse time series data and critically assess the fit
• Carry out a statistical analysis of time series data and interpret the results.

To enrol on this module, you must meet the following module-specific requirements:

• A 2:1 honours degree (or above) in Mathematics, Statistics or other mathematics-based subject, including modules covering probability theory, statistical inference and linear regression models
• A 2:2 honours degree in Mathematics, Statistics or other mathematics-based subject, including modules covering probability theory, statistical inference and linear regression models, supported by a CV and employer reference letter demonstrating a minimum of three years relevant professional experience.

Plus the additional standard entry requirements:

• A CV and personal statement outlining reasons for study
• English language at Band D (IELTS 6.5 overall with a minimum of 6.0 in reading/writing and 6.0 in listening/speaking). 

You will be assessed via coursework and examination, as follows:

• Coursework = 20%
• Examination = 80%

This is an on-campus module that involves a two-hour lecture and one-hour tutorial per week. You will be expected to attend both in person, though lectures are lecture-captured if you have strong impediments preventing you from attending. Exact times and dates will be provided upon enrolment. 

This module is offered as part of our flexible master’s awards in Professional Development. The awards are one of the most flexible currently offered in the UK, providing the opportunity to study a range of modules from across King’s, both on-campus and online. Whether you are looking for a promotion or to retrain, you have come to the right place.   

Designed for mature professionals juggling life and work commitments, our postgraduate awards will enable you to study at your own pace. In challenging financial times, you are also able to fund your studies module-by-module. We will support you to select the right module diet that meets your objectives while ensuring that you are well prepared for success. We will also help you to build your professional network of peers from across our suite of CPD modules.  

We can’t wait for you to continue your lifelong learning journey here at King’s.