| Unit name | Time Series Analysis |
|---|---|
| Unit code | MATH33800 |
| Credit points | 20 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Cho |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
MATH20800 Statistics 2 |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Why is this unit important?
Time series are observations on variables collected through time, such as daily temperature readings and hourly stock prices. Time series data are widely collected in many fields: for example in the pure sciences, medicine, marketing, economics and finance to name but a few. Time series data are different to the usual statistical data in that the observations are ordered in time and often are expected to be dependent over time. In fact, the interest lies in modelling such serial dependence by identifying a suitable model and make a prediction of the future based on the fitted model. The emphasis of this unit is on understanding, modelling and forecasting of time series data in both time and frequency domain.
Relation to Other Units
As with units Linear and Generalised Linear Models and Multivariate Analysis, this course is concerned with developing statistical methodology for a particular class of problems. Building upon the estimation and inferential tools developed for independent data, the unit equips you with tools for handling serially dependent data, which may be deemed as highly valued by employers. Also, the course would give you a good grounding if you wished to develop time series methods for a higher degree (e.g. PhD).
An overview of content
Identifying seasonality and trends, strict and second-order stationarity, autocorrelation and spectral density as measure of dependence in time and frequency domains, popular time series models such as ARMA, ARIMA and (G)ARCH, multivariate time series analysis
Learning outcomes
The students will be able to:
How will students, personally, be different as a result of the unit
Students will be able to use R for advanced statistical time series analyses and acquire enhanced mathematical modelling skills
The unit will be taught through a selection of lectures, online materials, independent activities such as problem sheets and other exercises, problem classes, support sessions and office hours.
Tasks which help you learn and prepare you for summative tasks (formative):
Weekly problem sheets
Tasks which count towards your unit mark (summative):
90% Timed examination 10% Coursework (selected questions from the problem sheet)
When assessment does not go to plan
If you fail this unit and are required to resit, reassessment is by a written examination in the Resit and Supplementary exam period.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MATH33800).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
