  An accurate prediction of crude oil prices over long future horizons is
challenging and of great interest to governments, enterprises, and investors.
This paper proposes a revised hybrid model built upon empirical mode
decomposition (EMD) based on the feed-forward neural network (FNN) modeling
framework incorporating the slope-based method (SBM), which is capable of
capturing the complex dynamic of crude oil prices. Three commonly used
multi-step-ahead prediction strategies proposed in the literature, including
iterated strategy, direct strategy, and MIMO (multiple-input multiple-output)
strategy, are examined and compared, and practical considerations for the
selection of a prediction strategy for multi-step-ahead forecasting relating to
crude oil prices are identified. The weekly data from the WTI (West Texas
Intermediate) crude oil spot price are used to compare the performance of the
alternative models under the EMD-SBM-FNN modeling framework with selected
counterparts. The quantitative and comprehensive assessments are performed on
the basis of prediction accuracy and computational cost. The results obtained
in this study indicate that the proposed EMD-SBM-FNN model using the MIMO
strategy is the best in terms of prediction accuracy with accredited
computational load.
