Forecasting economic and financial indicators by supply of deep and recovery neural networks

• Autorzy: Boyko N. , Ivanets A. , Bosik M.
• Języki publikacji: EN

Abstrakty

EN

This paper studies the potential of the application of the Recurrent Neural Networks, as well as the Deep Neural Networks in the field of the finances and trading. In particular, their use in the stock price predicting software. The concepts of the RNNs and DNNs are provided and explained thoroughly. Both techniques RNNs and DNNs are utilized in the implementation of the stock price predicting software. Two separate versions of the software are created in order to demonstrate the main differences between the algorithms, as well as to determine the best of the two. Each version is thoroughly examined. The comparison of each of the algorithms is performed and highlighted. Examples of the implementations of the software, utilizing each of the algorithms on big volumes of stock data, for stock price prediction are provided. The article summarizes the concept of stock price prediction backed by the popular machine learning algorithms and its application in the nowadays world.

Słowa kluczowe

EN

neural network; deep; recurrent; activation function; feedforward; neuron; hidden layer; stock price prediction

• Wydawca: Polish Academy of Sciences, Branch in Lublin
• Czasopismo: ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
• Rocznik: 2018
• Tom: Vol. 7, No 2
• Strony: 3--8
• Opis fizyczny: Bibliogr. 20 poz., rys., wykr.

Twórcy

autor

Boyko N.

• Lviv Polytechnic National University, Lviv, Ukraine

autor

Ivanets A.

• Lviv Polytechnic National University, Lviv, Ukraine

autor

Bosik M.

• Lviv Polytechnic National University, Lviv, Ukraine

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).