Some statistical and CI models to predict chaotic high-frequency financial data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Some statistical and CI models to predict chaotic high-frequency financial data
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Research team for modelling of economic and financial processes at VSB-TU Ostrava</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Intelligent and Fuzzy Systems
ISSN
1064-1246
e-ISSN
—
Volume of the periodical
39
Issue of the periodical within the volume
5
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
6419-6430
UT code for WoS article
000595520600037
EID of the result in the Scopus database
2-s2.0-85096966737