Research

Examples of developments and application in energy systems, finance, environmental systems and chemometrics:

The models we have developed, include models for:

To develop these applications we use methods from a range of different disciplines of which the most important are linear and non-linear time series analysis in discrete and continuous time, as well as linear and non-linear regression and non-parametric methods.

Among the more theoretical projects concerning methods to model stochastic, dynamical systems, we wish to mention the so-called grey-box modelling, which combine the deductive (deterministic) and inductive (stochastic) methods to model dynamical systems. Specifically, the Time Series group have developed methods and programs for identification and estimation of grey-box models.

Of other models we apply is Markov Chain models, ARIMA models, diffusion models, neural network and state space models, as well as a large range of non-linear and non-stationary models should be mentioned.
In connection to these models, investigation into and developments of model identification, parameter estimation and model validation methods are researched.

Watch us work.

Last modified 23 November 2004. Comments and suggestions to jkl@imm.dtu.dk