Just in time models for dynamical systems

The concept of just in time models is introduced for models that are not estimated until they are really needed. The idea is to store all observations of the process in a database, and then estimate a local model at the current working point. The variance/bias tradeoff is optimized locally by adapting the number of data and their relative weighting. This is in contrast to general nonlinear black-box models, like neural networks, where the performance is optimized globally.