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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 1029

ISBN

978-3-030-23755-4

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

9

Strana od-do

1315-1323

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Istanbul

Datum konání akce

23. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—