A Novel Approach to Time Series Complexity via Reservoir Computing
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-22695-3_31
Reference29 articles.
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3. Bianchi, F.M., Scardapane, S., Lokse, S., Jenssen, R.: Reservoir computing approaches for representation and classification of multivariate time series. IEEE Trans. Neural Netw. Learn. Syst. 32(5), 2169–2179 (2021). https://doi.org/10.1109/TNNLS.2020.3001377
4. Cao, Y., Tung, W.w., Gao, J.B., Protopopescu, V.A., Hively, L.M.: Detecting dynamical changes in time series using the permutation entropy. Phys. Rev. E. 70, 046217 (2004). https://doi.org/10.1103/PhysRevE.70.046217
5. Carroll, T.L.: Using reservoir computers to distinguish chaotic signals. Phys. Rev. E. 98, 052209 (2018). https://doi.org/10.1103/PhysRevE.98.052209
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