Comparing the performance of deep learning neural network architectures for predicting economic time series

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F23%3A10253148" target="_blank" >RIV/61989100:27510/23:10253148 - isvavai.cz</a>
Result on the web
<a href="https://www.ekf.vsb.cz/smsis/en/proceedings/past-proceedings/" target="_blank" >https://www.ekf.vsb.cz/smsis/en/proceedings/past-proceedings/</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Comparing the performance of deep learning neural network architectures for predicting economic time series
Original language description
We propose two deep learning algorithms for neural network models. The first one the Long short-term memory model and the second one convolutional neural network architecture. These architectures were designed for predicting time series and are evaluated on daily historical stock price data for Apple Inc. The datasets collected from oanda website were used as inputs. Both models were designed according to describing in theoretical background using toolkit of Keras and tested using MSE, RMSE. The achieved prediction accuracy obtained through the proposed deep learning convolutional neural network was much worse than long short-term memory model.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 15th International Conference on Strategic Management and its Support by Information Systems 2023: May 22-24, 2023, Ostrava, Czech Republic
ISBN
978-80-248-4687-3
ISSN
2570-5776
e-ISSN
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Number of pages
8
Pages from-to
192-199
Publisher name
VŠB - Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
May 22, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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