Forecasting Cryptocurrency Prices Using Support Vector Regression Enhanced by Particle Swarm Optimization

In the present study, a machine learning model based on support vector regression (SVR) is proposed for forecasting the closing price of the three most capitalized cryptocurrencies, i.e., Bitcoin, Ethereum, and Ripple. The optimal hyperparameters are obtained by applying the particle swarm optimization (PSO) algorithm, and four historical price characteristics are used as predictors, namely the opening, the highest, and the lowest prices, and the cryptocurrency trading volume. Based on a sample of daily cryptocurrency prices spanning from August 8, 2015 to May 10, 2019, the proposed PSO-SVR approach is tested and compared with a class of neural network algorithms such as the multi-layer perceptron, the long short-term memory and the bi-directional long short-term memory, all of which are optimized by PSO. The results obtained indicate that the novel PSO-SVR model significantly outperforms all the neural network rivals in forecasting.