Advanced Time Series Analysis

• Apply methods for building stochastic dynamic models
• Identify the need for a non-linear model
• Identify the need for a non-stationary model
• Knowledge about a number of non-linear and non-stationary model classes
• Establish models in discrete and continuous time
• Differentiate between methods for formulating stochastic models
• Apply stochastics models for prediction
• Knowledge about using stochastic differential equations for modelling
• Apply non-parametric and semi-parametric methods
• Calculate predictions of time series
• Estimate parameters in stochastic dynamic models
• Document and present results in a written report.

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. Model building for real life systems. The final contents of the course will be discussed with the students.

• 36 – 49 (Wed 8-17)

02417, Time series analysis (02417 or similar)

Lectures and exercises

International course.

Maximum: 40.

Please be aware that this course has a limited number of seats available. If there are too many applicants, a pool will be created for the remainder of the qualified applicants, and they will be selected at random. You will be informed 8 days before the start of the course, whether you have been allocated a spot.