| Type: | Open University
Language: English |
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Prerequisite: 04244/C0416
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Recommended semester: 7th - 9th semester
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Scope and form: Lectures and excercises
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Examination: One or several reports are evaluated (13-scale)
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Remarks: International course.
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Department: Informatics and Mathematical Modelling
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Aim: To give an introduction to advanced time series analysis. The primary goal to give a thorough knowledge on modelling dynamic systems. Special attention is paid on non-linear and non-stationary systems, and the use of stochastic differential equations for modelling physical systems.
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Contents: Non-linear time series models. Kernel estimators and time series analysis. Identification of non-linear models. State space models.
Prediction in non-linear models. State filtering. Stochastic differential equations. Estimation of linear and (some) non-linear stochastic differential equations. Experimental design for dynamic identification. Methods for tracking parameters in non-stationary time series. Examples of both non-linear and non-stationary models. Non-linear models and chaos.
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