Assessing machine learning performance in cryptocurrency market price prediction
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 99
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شناسه ملی سند علمی:
JR_JMMF-2-1_001
تاریخ نمایه سازی: 5 مهر 1401
چکیده مقاله:
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. ۴۳ variables consisting of ۲۸ technical indicators and t+۱۰ lags were calculated and appended to the Open, High, Low, Close and Volume (OHLCV) data for selected cryptocurrencies. Applying random forest as feature selection, ۲۵ variables were chosen, ۲۴ 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.
کلیدواژه ها:
نویسندگان
Kamran Pakizeh
Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
Arman Malek
Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
Mahya Karimzadeh khosroshahi
Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
Hasan Hamidi Razi
Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.