Forecasting bitcoin price fluctuations: a time series analysis approach for predictive modelling

Abstract

The recent fluctuations in the price of Bitcoin, marked by both significant increases and subsequent decreases, has attracted media and public attention. Consequently, many researchers have explored various factors influencing Bitcoin’s price and the underlying patterns of its fluctuations. This paper aims to predict and analyses the factors affecting Bitcoin’s price by creating a unique dataset with nearly 40 features and deriving two child datasets using correlation and mutual information as feature selection techniques. Additionally, we train machine learning models, including linear regression (LR), extreme gradient boosting (XGBoost), support vector regression (SVR), Facebook Prophet (FB Prophet), and bidirectional gated recurrent unit (BI-GRU), to predict Bitcoin’s next-day price. The model’s performance is evaluated using mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score metrics. Our findings indicate that machine learning techniques are effective in predicting Bitcoin’s price and could be valuable for investors seeking to maximize profits.