Course co-ordinator(s): Dr Fraser Daly (Edinburgh).
Aims:
This course aims to provide a good understanding of the concepts and methods used in time series analysis and advanced techniques for data analytics.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh.
Semester: 2.
Syllabus:
• Basic time series concepts and operators
• Stationary processes, general linear filter, autocorrelation function and spectrum
• MA, AR and ARMA processes
• ARIMA processes and Random Walk (RW) with or without drift
• Model estimation and model selection
• Models with trend and/or seasonality
• Forecasting
• Introduction to nonlinear processes
• Elementary principles of machine learning
• Copulas
• Extreme value theory
Learning Outcomes: Subject Mastery
On completion of this course the student should be able to:
• demonstrate knowledge of, and a critical understanding of, the main concepts of time series analysis
• demonstrate knowledge of, and a critical understanding of, the main properties of MA, AR, ARMA, ARIMA, and RW models
• use least squares, maximum likelihood and other methods to fit time series models to the data
• select proper model(s) using e.g. AIC or BIC
• fit trend and seasonal trend to the data, and fit time series models to the residuals
• understand methods used to produce forecasts
• understand ARCH, GARCH and other nonlinear time series models and their applications for modelling of financial data
• understand time series data well, and perform basic calculations and summaries of time series data
• understand and critically assess time series models fitted by computer packages
• use a range of time series models to produce forecasts
• understand the elementary principles of machine learning
• apply copulas to multivariate data
• understand the basic concepts of extreme value theory
Learning Outcomes: Personal Abilities
At the end of the course student should be able to:
• Demonstrate the ability to learn independently
• Manage time, work to deadlines and prioritise workloads
• communicate meaningfully and productively with others (including practitioners and professionals in the financial services industry) on data analytics issues
• use statistical software to fit time series models to data and
• analyse empirical data using modern data analytics techniques
SCQF Level: 11.
Credits: 15.
Other Information
Help: If you have any problems or questions regarding the course, you are encouraged to contact the course leader.
Canvas: further information and course materials are available on Canvas