| Recommended semester: 7th - 9th semester |
| Scope and form: Lectures and excercises |
| Evaluation: Approval of coursework/reports
|
| Examination: 13-scale |
| Previous course: 04444 |
| Prerequisites: Time series analysis |
| Preferred prerequisites: Knowledge of multivariate statistics |
| No credit points with: 04444 |
| 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. |
| 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. |
| Remarks: International course. |
Contact: Henrik Madsen, building 321, (+45) 4525 3408, hm@imm.dtu.dk
Jan Holst, tlf. +46 462229538, email: jahn@maths.lth.se |
| Department: 002 Informatics and Mathematical Modelling |
| Course URL: http://www.imm.dtu.dk/courses/02427 |
| Keywords: Non-linear and non-stationary systems, Stochastic differential equations, Higher order filters, Experimental design, Recursive estimation |
| Updated: 02-03-2001 |
