The Time Series Analysis Group
Examples of developments and application in energy systems,
systems and chemometrics:
Predictions of energy
in windmill parks
Predictions of interest
On-line control systems
The models we have developed, include models for:
The oxygen content in
Processes in waste water
Fuelling dynamics 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
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 other models we apply is Markov Chain models, ARIMA models,
models, neural network and state space models, as well as a large range
of non-linear and non-stationary models should be mentioned.
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In connection to these models, investigation into and developments
of model identification, parameter estimation and model validation
The Time Series homepage.
The Section for Statistics.
Last modified 23 November 2004. Comments and suggestions to firstname.lastname@example.org