Modeling time-varying processes by unfolding the time domain

Most current technologies in modeling time varying processes aim to adapt a static model over time in what has become to be known as continuous learning. We propose here a different approach to the same problem domain that includes the time explicitly in the modeling. An example implementation of this strategy is given in a form of a multilayer perceptron with explicit time input. The performance of this approach is evaluating on a benchmark that was constructed to illustrate typical problems in industrial applications