Forecasting of FOREX Price Trend Using Recurrent Neural Network - Long Short-term Memory
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50016858" target="_blank" >RIV/62690094:18450/20:50016858 - isvavai.cz</a>
Result on the web
<a href="https://digilib.uhk.cz/bitstream/handle/20.500.12603/212/Dobrovolny%20et%20al%20%281%29.pdf?sequence=1&isAllowed=y" target="_blank" >https://digilib.uhk.cz/bitstream/handle/20.500.12603/212/Dobrovolny%20et%20al%20%281%29.pdf?sequence=1&isAllowed=y</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.36689/uhk/hed/2020-01-011" target="_blank" >10.36689/uhk/hed/2020-01-011</a>
Alternative languages
Result language
angličtina
Original language name
Forecasting of FOREX Price Trend Using Recurrent Neural Network - Long Short-term Memory
Original language description
Algorithms of neural networks (NN) can search and represent both structured and not structured data, we employ then on financial time-series. This paper describes the use of Long short-term memory (LSTM) for FOREX pair EUR/USD price prediction. Aim of the paper is to test and proposes the best time block to predict based on a daily FOREX data. We employ the mean of absolute errors and the least mean squared errors to assess prediction results in order to find the time block. We tested time blocks from ten to fifty-eight days and 100 or 300 epochs. Training dataset contained daily exchange rate data from 1.4.1971 until 9.5.2019. The best performing network has been trained for 30-day period and 100 epochs. This paper also describes the effect of training for a high number of epochs.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
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
Article name in the collection
Hradec economic days 2020/1
ISBN
978-80-7435-776-3
ISSN
2464-6059
e-ISSN
2464-6067
Number of pages
9
Pages from-to
95-103
Publisher name
Gaudeamus
Place of publication
Hradec Králové
Event location
Hradec Králové
Event date
Apr 2, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000568108700011