Optimizing Precious Metal Price Forecasting with Hybrid Deep Learning Models: An Operational Research Perspective
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F25%3A00002900" target="_blank" >RIV/75081431:_____/25:00002900 - isvavai.cz</a>
Result on the web
<a href="https://actamont.fberg.tuke.sk/pdf/2025/n1/1kayathingal.pdf" target="_blank" >https://actamont.fberg.tuke.sk/pdf/2025/n1/1kayathingal.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Optimizing Precious Metal Price Forecasting with Hybrid Deep Learning Models: An Operational Research Perspective
Original language description
In the current study, Wolfram Mathematica is employed to study the application of seven deep neural network architectures in predicting precious metal prices: LSTM, CNN, MLP, MLP-CNN, MLP-LSTM, LSTM-CNN, and LSTM-CNN-LSTM. We will utilize a daily price series over ten years for gold, silver, and platinum. Model performances were evaluated using RMSE, MAPE, and RMSE metrics.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
50200 - Economics and Business
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Acta Montanistica Slovaca
ISSN
1335-1788
e-ISSN
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Volume of the periodical
30
Issue of the periodical within the volume
1
Country of publishing house
SK - SLOVAKIA
Number of pages
16
Pages from-to
1-16
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105011493790