Wavelet Analysis for Stock Market Forcasting
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F19%3APU132678" target="_blank" >RIV/00216305:26510/19:PU132678 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Wavelet Analysis for Stock Market Forcasting
Original language description
This paper deals with wavelet analysis and its application on the stock market. The time series of financial and economic data are usually non-linear and non-stationary. It has been shown that using decomposition models improves the prediction accuracy of these time series. These techniques include wavelet analysis, which decomposes data not only in the time domain but also in the frequency domain, and can predict non-periodic or non-stationary time series more accurately than Fourier transform. Given the decomposition of the time series using wavelet analysis, and last but not least attention is drawn to the advantages and disadvantages resulting from the use of the method in the financial markets.
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
50206 - Finance
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
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
Interdisciplinární mezinárodní vědecká konference doktorandů a odborných asistentů QUAERE 2019
ISBN
978-80-87952-30-6
ISSN
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e-ISSN
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Number of pages
1229
Pages from-to
149-153
Publisher name
Magnanimitas
Place of publication
Hradec Králové, Czech Republic
Event location
online
Event date
Jun 27, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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