A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting

To address time consuming and parameter sensitivity in the emerging decomposition-ensemble models, this paper develops a non-iterative learning paradigm without iterative training process. Different from the most existing decomposition-ensemble models using statistical or iterative approaches as individual forecasting tools, the proposed work otherwise employs the efficient and fast non-iterative algorithm random vector functional link (RVFL) network with randomly fixed weights and direct input-output links. Three major steps are included: decomposition via ensemble empirical mode decomposition (EEMD), prediction via RVFL network, and ensemble via linear addition. With crude oil price as studying sample, the proposed EEMD-based RVFL network performs significantly better in terms of prediction accuracy than not only single algorithms such as RVFL network, extreme learning machine (ELM), kernel ridge regression, random forest, back propagation neural network, least square support vector regression, and autoregressive integrated moving average, but also their respective EEMD-based ensemble variants. As for speed ranking, RVFL network developed in 1994 ranks the first among all the listed methods, and EEMD-based RVFL network defeats all the ensemble methods and most single methods, possibly due to the fact that RVFL network with direct input-output links needs far less enhancement nodes and hence a shorter computational time than those without the direct links such as the ELM developed in 2006. (C) 2017 Elsevier B.V. All rights reserved.