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
• IDocument 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.

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.