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%2F20%3A10246276" target="_blank" >RIV/61989100:27510/20:10246276 - isvavai.cz</a>

Výsledek na webu

<a href="https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs189107" target="_blank" >https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs189107</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/JIFS-189107" target="_blank" >10.3233/JIFS-189107</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

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 ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Výzkumný tým pro modelování ekonomických a finančních procesů na VŠB-TU Ostrava</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent and Fuzzy Systems

ISSN

1064-1246

e-ISSN

—

Svazek periodika

39

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

6419-6430

Kód UT WoS článku

000595520600037

EID výsledku v databázi Scopus

2-s2.0-85096966737