Cryptocurrency Price Prediction Based on ARIMA, Random Forest and LSTM Algorithm

Abstract

Price prediction of cryptocurrencies is bound to get more opportunities for investors engaged in digital currency-related industries in order to earn more revenue. In the traditional forecasting methods, the problem of the high volatility of bitcoin price needs to be effectively solved, making the forecasting accuracy become low and ineffective. Due to the rapid development of artificial intelligence technology, more and more relevant algorithms were used for cryptocurrency price research. This study would compare and analyze the prediction effect of the ARIMA time-series model, the Random Forest algorithm of machine learning, and the LSTM algorithm of deep learning algorithm on cryptocurrencies price prediction to assist investors in making investment decisions. In this paper, five years of time-series data of Bitcoin, Ether, and Dogecoin is obtained from 2018 to 2022. Then, the training set and testing set are separated with 0.8:0.2 to test ARIMA, Random Forest, and LSTM algorithms. To evaluate the model, the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and decidability coefficient (R²) are chosen as metrics to measure the prediction of each prediction model precision. Comparing the experimental results, the prediction accuracy of LSTM is better than that of Random Forest, and the prediction accuracy of Random Forest is better than ARIMA model. These results shed light on guiding further exploration of significant to cryptocurrency industry practitioners and visitors.