SemOpenAlex |

SemOpenAlex

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2603275890> ?p ?o ?g. }

Showing items 1 to 100 of ±137 with 100 items per page.

W2603275890 abstract "This paper propose that the combination of smoothing approach taking into account the entropic information provided by Renyi' method, has an acceptable performance in term of forecasting errors. The methodology of the proposed scheme is examined through benchmark chaotic time series, such as Mackay Glass, Lorenz, Henon maps, the Lynx and rainfall from Santa Francisca series, with addition of white noise by using neural networks-based energy associated (EAS) predictor filter modified by Renyi entropy of the series. In particular, when the time series is short or long, the underlying dynamical system is nonlinear and temporal dependencies span long time intervals, in which this are also called long memory process. In such cases, the inherent nonlinearity of neural networks models and a higher robustness to noise seem to partially explain their better prediction performance when entropic information is extracted from the series. Then, to demonstrate that permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series, computational results are evaluated against several non-linear ANN predictors proposed before to show the predictability of noisy rainfall and chaotic time series reported in the literature." @default.
W2603275890 created "2017-04-07" @default.
W2603275890 creator A5002927152 @default.
W2603275890 creator A5022419670 @default.
W2603275890 creator A5036246374 @default.
W2603275890 creator A5047945402 @default.
W2603275890 creator A5074617650 @default.
W2603275890 creator A5090072367 @default.
W2603275890 date "2016-11-01" @default.
W2603275890 modified "2023-10-17" @default.
W2603275890 title "Noisy chaotic time series forecast approximated by combining Reny's entropy with energy associated to series method: Application to rainfall series" @default.
W2603275890 cites W1559955294 @default.
W2603275890 cites W1643064630 @default.
W2603275890 cites W1643663407 @default.
W2603275890 cites W1661202143 @default.
W2603275890 cites W1720804347 @default.
W2603275890 cites W1967075906 @default.
W2603275890 cites W1978368241 @default.
W2603275890 cites W1987558424 @default.
W2603275890 cites W2014683958 @default.
W2603275890 cites W2021867701 @default.
W2603275890 cites W2022156534 @default.
W2603275890 cites W2038782607 @default.
W2603275890 cites W2050500382 @default.
W2603275890 cites W2050969514 @default.
W2603275890 cites W2067718885 @default.
W2603275890 cites W2073594296 @default.
W2603275890 cites W2079133700 @default.
W2603275890 cites W2092675251 @default.
W2603275890 cites W2103143406 @default.
W2603275890 cites W2108786107 @default.
W2603275890 cites W2111516480 @default.
W2603275890 cites W2114029912 @default.
W2603275890 cites W2119276589 @default.
W2603275890 cites W2125566231 @default.
W2603275890 cites W2135570582 @default.
W2603275890 cites W2141394518 @default.
W2603275890 cites W2146574767 @default.
W2603275890 cites W2161913203 @default.
W2603275890 cites W2279450699 @default.
W2603275890 cites W2284153934 @default.
W2603275890 cites W2303062067 @default.
W2603275890 cites W2329405819 @default.
W2603275890 cites W2402946813 @default.
W2603275890 cites W2463433918 @default.
W2603275890 cites W2508190783 @default.
W2603275890 cites W2566882829 @default.
W2603275890 cites W2896916590 @default.
W2603275890 cites W29896578 @default.
W2603275890 cites W3149561819 @default.
W2603275890 doi "https://doi.org/10.1109/la-cci.2016.7885702" @default.
W2603275890 hasPublicationYear "2016" @default.
W2603275890 type Work @default.
W2603275890 sameAs 2603275890 @default.
W2603275890 citedByCount "0" @default.
W2603275890 crossrefType "proceedings-article" @default.
W2603275890 hasAuthorship W2603275890A5002927152 @default.
W2603275890 hasAuthorship W2603275890A5022419670 @default.
W2603275890 hasAuthorship W2603275890A5036246374 @default.
W2603275890 hasAuthorship W2603275890A5047945402 @default.
W2603275890 hasAuthorship W2603275890A5074617650 @default.
W2603275890 hasAuthorship W2603275890A5090072367 @default.
W2603275890 hasConcept C104317684 @default.
W2603275890 hasConcept C105795698 @default.
W2603275890 hasConcept C106301342 @default.
W2603275890 hasConcept C112633086 @default.
W2603275890 hasConcept C11413529 @default.
W2603275890 hasConcept C121332964 @default.
W2603275890 hasConcept C143724316 @default.
W2603275890 hasConcept C151406439 @default.
W2603275890 hasConcept C151730666 @default.
W2603275890 hasConcept C154945302 @default.
W2603275890 hasConcept C158622935 @default.
W2603275890 hasConcept C185592680 @default.
W2603275890 hasConcept C197640229 @default.
W2603275890 hasConcept C2777052490 @default.
W2603275890 hasConcept C28826006 @default.
W2603275890 hasConcept C33923547 @default.
W2603275890 hasConcept C3770464 @default.
W2603275890 hasConcept C41008148 @default.
W2603275890 hasConcept C50644808 @default.
W2603275890 hasConcept C55493867 @default.
W2603275890 hasConcept C62520636 @default.
W2603275890 hasConcept C63479239 @default.
W2603275890 hasConcept C79337645 @default.
W2603275890 hasConcept C86803240 @default.
W2603275890 hasConceptScore W2603275890C104317684 @default.
W2603275890 hasConceptScore W2603275890C105795698 @default.
W2603275890 hasConceptScore W2603275890C106301342 @default.
W2603275890 hasConceptScore W2603275890C112633086 @default.
W2603275890 hasConceptScore W2603275890C11413529 @default.
W2603275890 hasConceptScore W2603275890C121332964 @default.
W2603275890 hasConceptScore W2603275890C143724316 @default.
W2603275890 hasConceptScore W2603275890C151406439 @default.
W2603275890 hasConceptScore W2603275890C151730666 @default.
W2603275890 hasConceptScore W2603275890C154945302 @default.
W2603275890 hasConceptScore W2603275890C158622935 @default.
W2603275890 hasConceptScore W2603275890C185592680 @default.
W2603275890 hasConceptScore W2603275890C197640229 @default.
W2603275890 hasConceptScore W2603275890C2777052490 @default.