Lernfähige Klassifikation von Zeitreihen

Holistic classification methods are presented, i.e., similarity measures will be used but no description of the curves by feature vectors of fixed length. Two different methods are adapted: construction of prototypes for a class and kNN classifier. In a preprocessing step the treated curves are approximated by spline functions. In the case where the measurements are taken from different time intervals, the curves are mapped onto symbol strings. In order to use the kNN method a distance measure in the set of finite strings is defined.