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Title: Stock Prices Dynamics Forecasting with Recurrent Neural Networks
Other Titles: Прогнозирование динамики изменения цен на акции с помощью рекуррентных нейронных сетей
Authors: Vasyaeva, T. A.
Martynenko, T. V.
Khmilovyi, S. V.
Andrievskaya, N. K.
Васяева, Т. А.
Мартыненко, Т. В.
Хмелевой, С. В.
Андриевская, Н. К.
Keywords: публикации ученых
machine learning
deep learning
recurrent neural networks
short-term memory
stock prices
Issue Date: 2020
Publisher: БГУИР, РБ
Citation: Stock Prices Dynamics Forecasting with Recurrent Neural Networks / Tatyana Vasyaeva [and other] // Открытые семантические технологии проектирования интеллектуальных систем = Open Semantic Technologies for Intelligent Systems (OSTIS-2020) : сборник научных трудов / Белорусский государственный университет информатики и радиоэлектроники ; редкол. : В. В. Голенков (гл. ред.) [и др.]. – Минск, 2020. – Вып. 4. – С. 277-282.
Abstract: The application of deep neural networks was examined in the area of stock prices forecasting of pharmacies chain "36 and 6". The learning sample formation in the time series area was shown and the neural network architecture was proposed. The neural network for exchange trade forecasting using Python’s Keras Library was developed and trained. The basic parameters setting of algorithm have been carried out.
Appears in Collections:OSTIS-2020

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