A Novel Kernel Regression-Based Decomposition Hybrid Framework for Forecasting Cryptocurrency Markets

Abstract

In financial time series markets, Bitcoin price forecasting is challenging due to the high nonlinearity, volatility, nonstationarity, and noise characterizing cryptocurrency markets. In this paper, we propose a novel hybrid linear/nonlinear forecasting model based on a kernel regression decomposition framework, which exploits nonparametric kernel smoothing methods and linear/nonlinear forecasting models to improve forecast accuracy. The Bitcoin price series is decomposed into the long-term trend and short-term fluctuation components using four kernel functions: Uniform, Gaussian, truncated Gaussian, and Epanechnikov. The decomposed components are modeled separately by linear models (AR and ARIMA) and nonlinear models (NPAR and NNAR), resulting in sixty-four hybrid forecasting combinations. The framework is tested on BTC/USD, BTC/GBP, BTC/EUR, BTC/CNY, and BTC/JPY markets through one-step-ahead forecasting, using different performance metrics and a statistical forecasting test. The empirical results reveal that the proposed decomposition-based hybrid models outperform the best forecasting models proposed in the literature, both single and direct-hybrid models, in all markets. The results confirm improved accuracy, robustness, and predictive reliability in forecasting cryptocurrency markets based on the proposed kernel-based decomposition.