Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices

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. •Proposing EMD–SBM–FNN for multi-step-ahead crude oil price forecasting•Providing empirical evidence on three multi-step-ahead prediction strategies•EMD–SBM–FNN using MIMO strategy is the best with accredited computational load.•Direct strategy and MIMO strategy achieve the best in terms of prediction accuracy.•Iterated strategy outperforms in terms of low computational load.