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ASSESSING MACHINE LEARNING PERFORMANCE IN CRYPTOCURRENCY MARKET PRICE PREDICTION | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 18 بهمن 1400 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2022.65626.1046 | ||
| نویسندگان | ||
Kamran Pakizeh1؛ Arman Malek1؛ Mahya Karimzadeh khosroshahi  1؛ Hasan Hamidi Razi2
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| 1Faculty of Financial Sciences, Kharazmi University, Tehran, Iran | ||
| 2Department of Hydraulics Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Cryptocurrency؛ Long short-term memory؛ Gated recurrent unit؛ Random forest classifier | ||
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آمار تعداد مشاهده مقاله: 119 |
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