On forecasting the intraday Bitcoin price using ensemble of variational mode decomposition and generalized additive model

• Published in: Journal of King Saud University. Computer and information sciences Vol. 34; no. 3; pp. 1003 - 1009
• Main Author: Gyamerah, Samuel Asante
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022; Springer
• Subjects: Bitcoin price forecasting; Empirical mode decomposition; Ensemble forecast; Generalized additive model; Variational mode decomposition; Bitcoin price forecasting; Variational mode decomposition; Empirical mode decomposition; Ensemble forecast; Generalized additive model
• ISSN: 1319-1578; 2213-1248; 2213-1248
• DOI: 10.1016/j.jksuci.2020.01.006

High frequency Bitcoin price series are often non-linear and non-stationary and hence forecasting the price of Bitcoin directly or by transformation using statistical models is subject to large errors. This paper presents an ensemble model using variational mode decomposition (VMD) and Generalized additive model (GAM) to forecast intraday Bitcoin price. To evaluate the performance of the constructed model, it is compared with an ensemble of empirical mode decomposition (EMD) and GAM. The results showed that VMD-GAM model performed better than the EMD-GAM ensemble model in terms of three evaluation metrics (root mean square error, mean absolute percentage error, and bias) used.