Bitcoin price prediction with machine learning

The most well-known cryptocurrency is Bitcoin, as it was the first cryptocurrency. The approximate value of the cryptocurrency market capitalization, of which 39% is Bitcoin, is 807 billion dollars (December 2022). It has become an important research topic due to the difficulty in predicting the price of Bitcoin due to the extreme volatility of the price, and there are many studies in the literature. For investors, this high volatility means high profits and risks. This thesis aims estimation results close to the actual price and reduces the risks for the investors, using machine learning methods, different data sets, and optimization techniques. The methods used: Multilayer Perceptron (MLP), Support Vector Machines (SVM), Generalized Regression Neural Network (GRNN), Recurrent Neural Network (RNN), Long-Short-Term Memory (LSTM), Gated Repetitive Unit (GRU), and Convolutional Neural Network (CNN). The thesis includes experiments with each machine learning model with the combinations of Bitcoin, gold, crude oil, natural gas, Ethereum, and dollar-euro parity. Hyper-parameter optimization methods such as Bayesian optimization (BO), random search, grid search, and Hparam parameters are examined. Models with BO achieved better results than others. This thesis proposes a new model for Bitcoin price prediction that effectively reduces prediction error. This new BO model with Gradient Incremental Regression Trees (GBRT), Gaussian Process (GP), Random Forest (RF), and Extra Trees (ET) was applied to optimizers and corresponding surrogate functions. In addition, to increase the comparability of the results with the other paper, it was evaluated with four different performance metrics: root square mean error (RMSE), mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In general, among the seven algorithms, predictions using only the closing price of Bitcoin yielded better results. In addition, we obtained very close results in the estimates made by adding Ethereum, crude oil, and natural gas to the data set. Better results were obtained with LSTM, CNN, and GRU, respectively, than with the other methods. The experimental optimization results indicated that hparam, grid search, and random search achieved the worst results in all four error metrics. BO-GP with hybrid LSTM-GRU outperformed all methods in this thesis and the examined literature for the value of MAE=0.002302, MAPE=0.005497, MSE=0.000015, and RMSE=0.003269.