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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units
Original language description
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.
Czech name
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Czech description
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Type
C - Chapter in a specialist book
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Advances on P2P, Parallel, Grid, Cloud and Internet Computing
ISBN
978-3-319-69834-2
Number of pages of the result
9
Pages from-to
787-795
Number of pages of the book
950
Publisher name
Springer, Cham
Place of publication
—
UT code for WoS chapter
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