Some statistical and CI models to predict chaotic high-frequency financial data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10242813" target="_blank" >RIV/61989100:27510/19:10242813 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/book/10.1007/978-3-030-23756-1" target="_blank" >https://link.springer.com/book/10.1007/978-3-030-23756-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-23756-1_154" target="_blank" >10.1007/978-3-030-23756-1_154</a>

Result language
angličtina
Original language name
Some statistical and CI models to predict chaotic high-frequency financial data
Original language description
Forecasting of financial time series data is a complex problem, which has benefited from recent advancements and research in machine learning. To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARMA models. As a competitive tool to statistical forecasting models, we use the popular classic neural network of perceptron type. To train neural networks, the BP algorithm and heuristics like genetic and micro-genetic algorithm are implemented. A comparative analysis of selected learning methods is also performed and evaluated. (C) 2020, Springer Nature Switzerland AG.
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Advances in Intelligent Systems and Computing. Volume 1029
ISBN
978-3-030-23755-4
ISSN
2194-5357
e-ISSN
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Number of pages
9
Pages from-to
1315-1323
Publisher name
Springer
Place of publication
Cham
Event location
Istanbul
Event date
Jul 23, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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