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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

04.12.2017

Time Series Classification in Reservoir- and Model-Space

verfasst von: Witali Aswolinskiy, René Felix Reinhart, Jochen Steil

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

We evaluate two approaches for time series classification based on reservoir computing. In the first, classical approach, time series are represented by reservoir activations. In the second approach, on top of the reservoir activations, a predictive model in the form of a readout for one-step-ahead-prediction is trained for each time series. This learning step lifts the reservoir features to a more sophisticated model space. Classification is then based on the predictive model parameters describing each time series. We provide an in-depth analysis on time series classification in reservoir- and model-space. The approaches are evaluated on 43 univariate and 18 multivariate time series. The results show that representing multivariate time series in the model space leads to lower classification errors compared to using the reservoir activations directly as features. The classification accuracy on the univariate datasets can be improved by combining reservoir- and model-space.
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Metadaten
Titel
Time Series Classification in Reservoir- and Model-Space
verfasst von
Witali Aswolinskiy
René Felix Reinhart
Jochen Steil
Publikationsdatum
04.12.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9765-5

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