Georgi Boshnakov
Lecturer in Statistics
- Alan Turing/1.125
- School of Mathematics
- University of Manchester
- Oxford Road, Manchester M13 9PL, UK
- georgi[dot]boshnakov[at]manchester.ac.uk
- Tel: +44 (0) 161 306 3684
- Fax: +44 (0) 161 275 5819
Page contents:
- School Responsibilities
- Research interests
- Publications
- Teaching
- Some R packages
- Some Emacs packages
- Undergraduate projects (3rd and 4th year)
- Postgraduate projects
- Professional affiliations
School Responsibilities:
Lecturing:
In the last ten years I have taught Stochastic Financial Modelling, Stochastic Processes,
Time Series, Statistics, Linear and Generalized Linear Models, Introduction to Computing,
Matlab as well as wide range of mathematical methods courses for engineering students in
various departments.
Computing:
I am acting as Departmental Adviser on Computing (Mathematica, Matlab, some statistical
packages) and LaTeX.
Admin:
I am co-organiser of the Probability and Statistics Research Seminars at the School.
Please contact me if you have an idea about a statistics talk. Proposals from visitors to
UK are particularly welcome.
Full details about the seminars are available
here.
We are organising workshop New Developments In Time Series and Spatio-Temporal Analysis.
Environment:
The papers and the material in the course pages are typeset exlusively with LaTeX/pdfLaTeX. My development environment is Emacs equiped with the packages AucTeX and ESS. Computations are done using the statistical system R and Mathematica, unless other systems are explicitly referred to (even then, I may be cheating somewhat). Many thanks to the developers and maintainers of Emacs, LaTeX (and friends) and R. Mathematica is also an wonderful system but my thanks go to our University for paying for the license.
Research interests
Time Series Analysis and forecasting, Data Analysis, Statistical computing, Matrices in statistics, Symbolic computation.
Teaching
Course material
You may be asked for identification to get access to some of the pages below.- Time Series Analysis (MATH38032)
- Time Series Analysis and forecasting in finance (MATH48032/MATH68032)
- Practical Statistics I (MATH20812)
Computing related material
Course materials from previous academic years.
Material for some courses that I am no longer teaching or that have been discontinued. Solutions to example sheets and assessment material have been removed.- Linear Statistical Models (MATH38011)
- COMP11120: Mathematical Techniques for Computer Science (probability component)
Undergraduate projects
I offer projects on time series topics and, in particular, financial time series. The projects require background in time series and in most cases this means that students should do (or have done) the Applied time series (ATS) or similar course.
MMath students wishing to do such projects in their 4th year will improve their prospects by planning ahead and taking the ATS course in their 3rd year.
1st and 2nd year students reading this section and planning their options should notice that the Statistical methods course in 2nd Sem/2nd year is a prerequisite for (essentially) all subsequent statistics courses. Practical statistics I is a big plus too.
PhD and MSc projects
I mainly offer projects which fall under the umbrella of "time series analysis". For other topics in statistics and probability (usually motivated by my research on time series) see my recent publications and research reports.
Here is an example project.
Time series data, such as economic indicators and stock prices, are now routinely collected in vast quantities. This has opened a number of new problems of interest to financial institutions, governments and academy. One such problem is clustering of time series. For example, splitting industries or stock market companies into groups (clusters) of entities, similarly performing over a period of time, according to some indicator, may be helpful for decision and policy makers.
Time series clustering is an area of active research. We will be interested mainly in developing methodology and algorithms for dimensionality reduction, e.g. extracting a vector of features from a time series which may then be fed to appropriate clustering or classification algorithms. Such features may include, among others, trends, seasonality, autocovariance/spectral structure, long memory indices, and even whole models, such as ARIMA. Also, there is no universal set of features for this clustering problem.
Note: Do not tell me that you are fond of time series if you have not taken such courses during your previous studies. Good mathematical and/or computational background is top priority.
Professional affiliations
- Fellow of the Royal Statistical Society
- Member of the Bulgarian Statistical Society