Bitcoin daily close price prediction using optimized grid search method

【Author】 Rostami, Marzieh; Bahaghighat, Mahdi; Mohammadi Zanjireh, Morteza

【Source】ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA

【影响因子】0.000

【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 Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.

【Keywords】bitcoin; cryptocurrency; price prediction; autoregression (AR); moving average (MA); ARIMA

【发表时间】2021 DEC 1

【收录时间】2022-02-08

【文献类型】期刊

【主题类别】

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