A novel hybrid model for forecasting crude oil price based on time series decomposition

Oil price forecasting has received a prodigious attention by scholars and policymakers due to its significant effect on various economic sectors and markets. Incentivized by this issue, the author proposes a novel hybrid model for crude oil price forecasting whose focus is on improving the accuracy of prediction taking into consideration the characteristics existing in the oil price time series. In so doing, the author constitutes a hybrid model consisting of complete ensemble empirical mode decomposition, support vector machine, particle swarm optimization, and Markov-switching generalized autoregressive conditional heteroskedasticity to capture the nonlinearity and volatility of the time series more effectively. Mean absolute error, root mean square error, and mean absolute percentage error tests are used to measure forecasting errors. Results robustness and forecasting quality of the proposed hybrid model compared with counterparts are also investigated by Diebold-Mariano test. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models.