Intelligence in Finance and Economics for Predicting High-Frequency Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F23%3A10251545" target="_blank" >RIV/61989100:27510/23:10251545 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/math11020454" target="_blank" >https://doi.org/10.3390/math11020454</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/math11020454" target="_blank" >10.3390/math11020454</a>
Alternative languages
Result language
angličtina
Original language name
Intelligence in Finance and Economics for Predicting High-Frequency Data
Original language description
Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time. (C) 2023 by the authors.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10100 - Mathematics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Mathematics
ISSN
2227-7390
e-ISSN
2227-7390
Volume of the periodical
11
Issue of the periodical within the volume
2
Country of publishing house
CH - SWITZERLAND
Number of pages
15
Pages from-to
454
UT code for WoS article
000927070300001
EID of the result in the Scopus database
2-s2.0-85146809839