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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

  • 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

    10100 - Mathematics

Result continuities

  • Project

  • 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