A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

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The VEC-NAR model for short-term forecasting of oil prices (2019, background): “Mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) are commonly used to estimate the predictive performance of forecasting models (Cheong, 2009; Mohammadi and Su, 2010; Movagharnejad et al., 2011; Jammazi and Aloui, 2012; Suganthia and Samuel, 2012; Xu and Ouennich”
Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression (2017, methodology): “Consequently, 25 Inspired by Tang et al. (2012)[18], Guo et al. (2012)[28], Zhu and Wei,(2013)[10] and Yu et al. (2015)[39], two single 26 models (LSSVR and ARIMA), a hybrid ARIMA+LSSVR model, and four multiscale forecasting models (EMD–27 ARIMA–IMF1–ADD, EMD–ARIMA–ADD, EMD–LSSVR–IMF1–ADD and…”