Acta Universitatis Sapientiae: Informatica | |
Bitcoin daily close price prediction using optimized grid search method | |
Bahaghighat Mahdi1  Zanjireh Morteza Mohammadi2  Rostami Marzieh3  | |
[1] Department of Computer Engineering, Imam Khomeini International University, Qazvin, Iran;Department of Computer Engineering, Imam Khomeini International, University, Qazvin, Iran;Department of Information Technology Engineering, Raja University of Qazvin, Qazvin, Iran; | |
关键词: bitcoin; cryptocurrency; price prediction; autoregression (ar); moving average (ma); arima; 68r15; | |
DOI : 10.2478/ausi-2021-0012 | |
来源: DOAJ |
【 摘 要 】
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 Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.
【 授权许可】
Unknown