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Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F19%3A00001518" target="_blank" >RIV/75081431:_____/19:00001518 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1051/shsconf/20196101006" target="_blank" >http://dx.doi.org/10.1051/shsconf/20196101006</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1051/shsconf/20196101006" target="_blank" >10.1051/shsconf/20196101006</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

  • Original language description

    The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    50202 - Applied Economics, Econometrics

Result continuities

  • Project

  • Continuities

    N - Vyzkumna aktivita podporovana z neverejnych zdroju

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    SHS Web of Conferences: Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018)

  • ISBN

    9782759890637

  • ISSN

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

  • Publisher name

    EDP Sciences

  • Place of publication

    Les Ulis, France

  • Event location

    Beijing, PR China

  • Event date

    Nov 8, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000467727800006