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To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the e®ective \decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is¯rst applied to divide the original data (i.e., di±cult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the¯nal prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity.

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In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

Previous survey papers offer knowledge of deep learning hardware devices and software frameworks. This paper introduces benchmarking principles, surveys machine learning devices including GPUs, FPGAs, and ASICs, and reviews deep learning software frameworks. It also reviews these technologies with respect to benchmarking from the angles of our 7-metric approach to frameworks and 12-metric approach to hardware platforms.After reading the paper, the audience will understand seven benchmarking principles, generally know that differential characteristics of mainstream AI devices, qualitatively compare deep learning hardware through our 12-metric approach for benchmarking hardware, and read benchmarking results of 16 deep learning frameworks via our 7-metric set for benchmarking frameworks.