Acta Univ. Sapientiae, Informatica 13, 2 (2021) 265–287 DOI: 10.2478/ausi-2021-00012
Bitcoin daily close price prediction using optimized grid search method Marzieh ROSTAMI Department of Information Technology Engineering, Raja University of Qazvin, Qazvin, Iran email: mrz.rst@gmail.com
Mahdi BAHAGHIGHAT1
Morteza MOHAMMADI ZANJIREH
Department of Computer Engineering, Imam Khomeini International University, Qazvin, Iran email: Bahaghighat@eng.ikiu.ac.ir
Department of Computer Engineering, Imam Khomeini International University, Qazvin, Iran email: Zanjireh@eng.ikiu.ac.ir
Abstract. Cryptocurrencies are digital assets that can be stored and transferred electronically. Bitcoin (BTC) is one of the most popular cryptocurrencies that has attracted many attentions. The BTC price is considered as a high volatility time series with non-stationary and non-linear behavior. Therefore, the BTC price forecasting is a new, challenging, and open problem. In this research, we aim the predicting price using machine learning and statistical techniques. We deploy several robust approaches such as the Box-Jenkins, Autoregression (AR), Moving Average (MA), ARIMA, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and Grid Search algorithms to predict BTC price. To evaluate the performance of the proposed model, Forecast Error (FE), Mean Computing Classification System 1998: G.2.2 Mathematics Subject Classification 2010: 68R15 Key words and phrases: bitcoin; cryptocurrency; price prediction; autoregression (AR); moving average (MA), ARIMA 1 Corresponding author
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