Kamran Pakizeh; Arman Malek; Mahya Karimzadeh khosroshahi; Hasan Hamidi Razi
Abstract
Cryptocurrencies, which are digitally encrypted and decentralized,
continue to attract attention of financial market players across
the world. Because of high volatility in cryptocurrency market, predicting
price of cryptocurrencies has become one of the most complicated
fields in financial markets. ...
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Cryptocurrencies, which are digitally encrypted and decentralized,
continue to attract attention of financial market players across
the world. Because of high volatility in cryptocurrency market, predicting
price of cryptocurrencies has become one of the most complicated
fields in financial markets. In this paper, we use Long Short-Term Memory
(LSTM) and Gated Recurrent Unit (GRU) models to predict price
of four well-known cryptocurrencies of Bitcoin (BTC), Ethereum (ETH),
Litecoin (LTC), and Ripple (XRP). These models are subdivisions of Artificial
Intelligence, machine learning and data science. The main aim of
this paper is to compare the accuracy of above-mentioned models in forecasting
time series data, to find out which model can better predict price
in these four cryptocurrencies. 43 variables consisting of 28 technical
indicators and t+10 lags were calculated and appended to the Open,
High, Low, Close and Volume (OHLCV) data for selected cryptocurrencies.
Applying random forest as feature selection, 25 variables were
chosen, 24 of them selected as feature (independent variables) and one
as a dependent variable. Each attribute value was converted into a relative
standard score, followed by Min-max scaling; we compare models
and results of Dieblod Mariano test that is used to examine whether the
differences in predictive accuracy with these two models are significant,
reveal that LSTM reaches better accuracy than GRU for BTC and ETH,
but both models convey the same accuracy for LTC and XRP.
