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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%2F17%3A00315468" target="_blank" >RIV/68407700:21220/17:00315468 - 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>

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

    2017

  • 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

  • e-ISSN

  • 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