Comparing SPO-tuned GP and NARX Prediction Models for Stormwater Tank Fill Level Prediction

Publication: Oliver Flasch, Thomas Bartz-Beielstein, Artur Davtyan, Patrick Koch and Wolfgang Konen, Comparing SPO-tuned GP and NARX Prediction Models for Stormwater Tank Fill Level Prediction. In P. Sobrevilla (ed.), Proc. WCCI, July 2010, Barcelona (PDF)

Abstract:

Slow Feature Analysis (SFA) has been established
as a robust and versatile technique from the neurosciences to
learn slowly varying functions from quickly changing signals.
Recently, the method has been also applied to classification
tasks. Here we apply SFA for the first time to a time series
classification problem originating from gesture recognition. The
gestures used in our experiments are based on acceleration
signals of the Bluetooth Wiimote controller (Nintendo). We
show that SFA achieves results comparable to the well-known
Random Forest predictor in shorter computation time, given
a sufficient number of training patterns. However – and this
is a novelty to SFA classification – we discovered that SFA
requires the number of training patterns to be strictly greater
than the dimension of the nonlinear function space. If too few
patterns are available, we find that the model constructed by
SFA severely overfits and leads to high test set errors. We
analyze the reasons for overfitting and present a new solution
based on parametric bootstrap to overcome this problem.