An Integrative System Based on Signal Processing and Tuned Regression Gaussian Process by Grey Wolf Optimization Algorithm for Bitcoin Price Forecasting

We propose various hybrid predictive systems to forecast the Bitcoin next-day price. In particular, we combine the decomposition methods based on signal processing techniques including maximum overlap discrete wavelet transform (MODWT), empirical wavelet transform (EWT), empirical mode decomposition (EMD), and variational mode decomposition (VMD) for feature extraction from original price series. Then, the extracted features are fed to the machine learning models for training and forecasting. We implemented five machine learning models, including regression Gaussian process (RGP), support vector regression (SVR), k-nearest neighbors algorithm (kNN), regression trees (RT), and feedforward neural networks (FFNN). The grey wolf optimization (GWO) algorithm is employed for hyperparameter optimization of the machine learning models. The root mean squared error (RMSE) is used for the evaluation and comparison of 20 hybrid predictive systems. The simulation results show that the RGP-GWO-VMD hybrid predictive system achieved the lowest forecasting error. In addition, RGP-GWO yielded on average the lowest forecasting error across all of the machine learning systems. Furthermore, among signal decomposition methods, the lowest forecasting error is generally achieved under the EWT. Hence, we presented the best results in forecasting Bitcoin prices from 20 hybrid prediction systems to serve as the baseline for future work and to guide traders, investors, and portfolio managers.