Aswolinskiy, WitaliWitaliAswolinskiyReinhart, René FelixRené FelixReinhartSteil, JochenJochenSteil2022-03-132022-03-132016https://publica.fraunhofer.de/handle/publica/39571910.1007/978-3-319-46182-3_172-s2.0-84988000678Learning in the space of Echo State Network (ESN) output weights, i.e. model space, has achieved excellent results in time series classification, visualization and modelling. This work presents a systematic comparison of time series classification in the model space and the classical, discriminative approach with ESNs. We evaluate the approaches on 43 univariate and 18 multivariate time series. It turns out that classification in the model space achieves often better classification rates, especially for high-dimensional motion datasets.enconference paper