| dc.contributor.author | Tomčala, Jiří | |
| dc.date.accessioned | 2019-05-13T12:06:03Z | |
| dc.date.available | 2019-05-13T12:06:03Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Journal of Supercomputing. 2019, vol. 75, issue 3, special issue, p. 1443-1454. | cs |
| dc.identifier.issn | 0920-8542 | |
| dc.identifier.issn | 1573-0484 | |
| dc.identifier.uri | http://hdl.handle.net/10084/134901 | |
| dc.description.abstract | This paper concentrates on the entropy estimation of time series. Two new algorithms are introduced: Fast Approximate Entropy and Fast Sample Entropy. Their main advantage is their lower time complexity. Examples considered in the paper include interesting experiments with real-world data obtained from IT4Innovations' supercomputers Salomon and Anselm, as well as with data artificially created specifically to test the credibility of these new entropy analyzers. | cs |
| dc.language.iso | en | cs |
| dc.publisher | Springer | cs |
| dc.relation.ispartofseries | Journal of Supercomputing | cs |
| dc.relation.uri | https://doi.org/10.1007/s11227-018-2657-2 | cs |
| dc.rights | © Springer Science+Business Media, LLC, part of Springer Nature 2018 | cs |
| dc.subject | entropy | cs |
| dc.subject | approximate entropy | cs |
| dc.subject | sample entropy | cs |
| dc.subject | Fast Approximate Entropy | cs |
| dc.subject | Fast Sample Entropy | cs |
| dc.title | Acceleration of time series entropy algorithms | cs |
| dc.type | article | cs |
| dc.identifier.doi | 10.1007/s11227-018-2657-2 | |
| dc.type.status | Peer-reviewed | cs |
| dc.description.source | Web of Science | cs |
| dc.description.volume | 75 | cs |
| dc.description.issue | 3 | cs |
| dc.description.lastpage | 1454 | cs |
| dc.description.firstpage | 1443 | cs |
| dc.identifier.wos | 000463635700034 | |