Assessing machine learning performance in cryptocurrency market price prediction

Cryptocurrencies, which are digitally encrypted and decentralized, con- tinue 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 finan- cial 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 forecast- ing time series data, to find out which model can better predict price in these four cryptocurrencies. 43 variables consisting of 28 technical indica- tors and t+10 lags were calculated and appended to the Open, High, Low, Close and Volume (OHLCV) data for selected cryptocurrencies. Apply- ing 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