Mathematical Statistics: Non-linear Time Series Analysis

The course “Mathematical Statistics: Nonlinear Time Series” offers in-depth knowledge of analyzing and modeling nonlinear time series. You will learn to distinguish between linear and nonlinear models and apply methods such as Kalman filters and particle filters to handle latent processes. This is an elective advanced-level course for a Master’s degree in Mathematical Statistics. The language of instruction is Swedish and English. If needed, the course will be given entirely in English. The course has prerequisites, read more about these in the syllabus.

By understanding and applying these advanced methods, you will be well prepared to solve complex problems in both academic research and industrial applications.

The studies include lectures, exercises, laboratory work, and project work. Lectures provide theoretical knowledge, while exercises and labs allow you to apply theory in practice. The project work gives you the opportunity to work with real data and develop your skills in modeling and analysing nonlinear time series. Assessment is based on written and oral presentations of the project work. To pass the course, you must participate in all mandatory components and submit an approved project report.

Prerequisites

Knowledge corresponding to MASM17 Mathematical Statistics: Time Series Analysis, 7,5 ECTS, and English course 6/B.

Tuition fees for non-EU/EEA citizens

Citizens of countries outside:

• The European Union (EU)
• The European Economic Area (EEA) and
• Switzerland

are required to pay tuition fees. You pay an instalment of the tuition fee in advance of each
semester.

Tuition fees, payments and exemptions

Full programme/course tuition fee: SEK 21,250
First payment: SEK 21,250

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Note that you may also need to pay an application fee, or provide proof of exemption.

Application fee

No tuition fees for citizens of the EU, EEA and Switzerland

There are no tuition fees for citizens of the European Union (EU), the European Economic Area (EEA) and Switzerland.