DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES - Problems of Information Technology

DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES - Problems of Information Technology

DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES - Problems of Information Technology

DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES - Problems of Information Technology

DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES - Problems of Information Technology
DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№1, 2020

DEEP LSTM METHOD FOR FORECASTING BITCOIN PRICES

Yadigar N. Imamverdiyev

This study proposes a deep neural network architecture for forecasting the Bitcoin exchange price, which is based on LSTM (Long-Short Term Memory) units, one of the types of recurrent neural networks. In recent years, cryptocurrencies have become important financial instruments, and the problem of forecasting cryptocurrency exchange rates for traditional currencies has become very urgent. Bitcoin is the first cryptocurrency, and more than half of the total cryptocurrency capitalization belongs to it and it plays the role of gold in the world of cryptocurrencies, that is, the price of other cryptocurrencies is often expressed in bitcoins. Based on these considerations, the problem of forecasting the Bitcoin exchange rate is considered, and the proposed deep LSTM architecture, in the experiments with real high-volume data spanning several years, shows better results than the statistical methods commonly used in forecasting of time series. The results obtained are important both for ordinary users interested in cryptocurrencies and for investors who are actively working in the field of cryptocurrencies. The results also show that deep learning approaches can be very productive when used in other tasks of the intellectual analysis of non-stationary time series on cryptocurrencies (pp.82-89).

Keywords: Bitcoin, cryptocurrency, time-series, forecasting, deep learning, recurrent neural networks, LSTM.
DOI : 10.25045/jpit.v11.i1.10
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