Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F18%3A00315468" target="_blank" >RIV/68407700:21220/18:00315468 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21220/17:00315468
Result on the web
<a href="https://doi.org/10.1007/978-3-319-69835-9_74" target="_blank" >https://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>
Alternative languages
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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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 12th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2017)
ISBN
978-3-319-69835-9
ISSN
2367-4512
e-ISSN
2367-4512
Number of pages
9
Pages from-to
787-795
Publisher name
Springer International Publishing AG
Place of publication
Cham
Event location
Barcelona
Event date
Nov 8, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000464606800074