| Recommended semester: 4th -7th semester |
| Scope and form: Lectures and excercises. |
Evaluation: Written exam and approval of coursework
Two mandatory assignments that count 40% in the grading. |
| Examination: 13-scale |
| Previous course: 04244 |
| Prerequisites: Elementary statistics |
| No credit points with: 04244 / C0416 |
| Aim: To give a thorough introduction to time series analysis generally relevant to engineering science. A special attention is put on methods for model formulation and estimation. A main goal is to form the theoretical background for applications within forecasting, automatic and adaptive control, image analysis, econometrics and technometrics. |
| Contents: Linear stochastic processes. Conditional expectations with applications. Characterisation of stochastic processes. Second order analysis. Description in time and frequency domain. Correlation functions and their applications. Model formulation. Non-stationary processes. Time series with periodic variations and trends. Identification, estimation and verification of models for stochastic processes. Box-Jenkins method. Spectral analysis. Bivariate and multivariate time series analysis. Transfer functions with stochastic models. State space formulation, prediction and reconstruction. Kalman filter. Time series with missing observations. Methods for recursive estimation. Adaption methods. Introduction to non-linear stochastic processes. Theoretical results are combined with examples of practical applications. |
| Remarks: The course provides a good background for a number of activites such as proces control, dynamic simulation, optimal control, analysis of signals, modelling and system identification. |
| Contact: Henrik Madsen, building 321, (+45) 4525 3408, hm@imm.dtu.dk |
| Department: 002 Informatics and Mathematical Modelling |
| Course URL: http://www.imm.dtu.dk/courses/02417 |
| Keywords: time series analysis, dynamic system modelling, correlation functions, prediction, Kalman filters |
| Updated: 20-04-2001 |
