**
Harri Valpola
Neural Networks Research Centre
Helsinki University of Technology
P.O.Box 5400, 02015 HUT, Finland
Email: Harri.Valpola@hut.fi
URL: http://www.cis.hut.fi/harri/**

This technical report describes how the nonlinear factor
analysis algorithm introduced in [4] can be extended by
taking into account the dynamics of the factors. The resulting
algorithm represents the observations as having been generated by a
nonlinear dynamical system. The internal states and the nonlinear
mappings describing the nonlinear system are assumed to be unknown
and they are estimated from the observations. Experiments with
artificial data verify that the algorithm is able to reconstruct the
dynamics of the underlying process which has generated the
observations.

- Introduction
- Predictable factors and state-space models
- Model and learning algorithm
- Results
- Discussion
- Acknowledgements
- Bibliography
- About this document ...