Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F17%3A00322293" target="_blank" >RIV/68407700:21220/17:00322293 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-69835-9_74" target="_blank" >http://dx.doi.org/10.1007/978-3-319-69835-9_74</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-69835-9_74" target="_blank" >10.1007/978-3-319-69835-9_74</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Popis výsledku v původním jazyce

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

Název v anglickém jazyce

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Popis výsledku anglicky

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Advances on P2P, Parallel, Grid, Cloud and Internet Computing

ISBN

978-3-319-69834-2

Počet stran výsledku

9

Strana od-do

787-795

Počet stran knihy

950

Název nakladatele

Springer, Cham

Místo vydání

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Kód UT WoS kapitoly

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