Multi step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods
Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data driven approaches. The objective of this paper is to develop a novel decomposition ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi step ahead. The algorithms are k Nearest Neighbors, Support Vector Regression with Linear kernel, Feed forward Artificial Neural Network with single layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR) principal component analysis (PCA) and Box Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD STACK CORR, VMD STACK PCA, and VMD STACK BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one day ahead forecast VMD STAK BOXCOX model presented the better performance, and for two and three days ahead forecast VMD STACK CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD STACK CORR, VMD STACK PCA, and VMD STACK BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models)
2020 International Joint Conference on Neural Networks (IJCNN)