Forecasting of High Frequency Data Using Statistical and Neural Network Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F12%3A%230004398" target="_blank" >RIV/47813059:19240/12:#0004398 - 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
Forecasting of High Frequency Data Using Statistical and Neural Network Models
Original language description
The paper intends to forecast the high frequency time series data by two approaches. In the first one, the ARCH/GARCH methodology is applied. In the second one, the various types of gtanular RBF network are used to predict EUR/USD rates. The summary statistics are assesed in forecastind models. Our comparison result shows that the proposed approaches achieve good accuracy on the dataset.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICT for competitiveness 2012
ISBN
978-80-7248-731-8
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
176-183
Publisher name
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Place of publication
Karviná, ČR
Event location
Karviná, ČR
Event date
Jan 1, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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