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Pakizeh, Kamran, Malek, Arman, Karimzadeh khosroshahi, Mahya, Hamidi Razi, Hasan. (1401). Assessing machine learning performance in cryptocurrency market price prediction. سامانه مدیریت نشریات علمی, 2(1), 1-32. doi: 10.22054/jmmf.2022.14563
Kamran Pakizeh; Arman Malek; Mahya Karimzadeh khosroshahi; Hasan Hamidi Razi. "Assessing machine learning performance in cryptocurrency market price prediction". سامانه مدیریت نشریات علمی, 2, 1, 1401, 1-32. doi: 10.22054/jmmf.2022.14563
Pakizeh, Kamran, Malek, Arman, Karimzadeh khosroshahi, Mahya, Hamidi Razi, Hasan. (1401). 'Assessing machine learning performance in cryptocurrency market price prediction', سامانه مدیریت نشریات علمی, 2(1), pp. 1-32. doi: 10.22054/jmmf.2022.14563
Pakizeh, Kamran, Malek, Arman, Karimzadeh khosroshahi, Mahya, Hamidi Razi, Hasan. Assessing machine learning performance in cryptocurrency market price prediction. سامانه مدیریت نشریات علمی, 1401; 2(1): 1-32. doi: 10.22054/jmmf.2022.14563

Assessing machine learning performance in cryptocurrency market price prediction

Journal of Mathematics and Modeling in Finance
مقاله 1، دوره 2، شماره 1، مهر 2022، صفحه 1-32  XML اصل مقاله (790.73 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22054/jmmf.2022.14563
نویسندگان
Kamran Pakizeh1؛ Arman Malek1؛ Mahya Karimzadeh khosroshahi email 1؛ Hasan Hamidi Razi2
1Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
2Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
چکیده
Cryptocurrencies, which are digitally encrypted and decentralized, continue to attract attention of  nancial market players across the world. Because of high volatility in cryptocurrency market, predicting price of cryptocurrencies has become one of the most complicated  elds in  nan-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 Arti cial 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  nd 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 signi cant, reveal that LSTM reaches better accuracy than GRU for BTC and ETH, but both models convey the same accuracy for LTC and XRP.
کلیدواژه‌ها
Cryptocurrency؛ Long short-term memory؛ Gated recurrent unit؛ Random forest classifi er
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