Research on prediction of bitcoin price based on machine learning methods

Shanghai University, Sydney Institute of Language and Commerce, Shanghai, 200444, China

^* Corresponding author: zcl1604156517@shu.edu.cn

Abstract

Bitcoin, a decentralized digital currency, has gained widespread acceptance and recognition in recent years. The prediction of Bitcoin prices is a challenging task due to its relatively young age and high volatility. Therefore, this study explores the accuracy of price prediction for Bitcoin using machine learning models and makes comparsion on the outcome of different models, Linear Regression, Long Short-Term Memory, and Recurrent Neural Network. This study utilizes the closing price of Bitcoin in USD from a Kaggle dataset as the independent variable. The study also adopts Mean Absolute Error (MAE) as the measurement indicators, and comparative performance analysis is conducted under various circumstances. The experimental results demonstrate that LR performs poorly in Bitcoin price prediction, while LSTM and RNN outperform LR. Further analysis reveals that LSTM performs better during price apexes, while RNN performs better during price recessions. Graphical representations illustrate the strengths and weaknesses of each model under different market scenarios. Through comparison, the article provides an insight for other researchers to choose corresponding machine learning models under different circumstances to predict bitcoin price.