A new approach for crude oil price analysis based on Empirical Mode Decomposition

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A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015, background): “Typically, the main factors hidden in economic data can be referred to cyclical (including seasonal) patterns, mutable patterns (sudden changes by extreme events), and central trend [24,39].”
Asymmetric Impact of Oil Price Shock on Stock Market in China: A Combination Analysis Based on SVAR Model and NARDL Model (2017, background): “The short-term adjustments are more likely to be a result of uncertainties such as war and weather changes than market supply and demand (Zhang, Lai, and Wang 2008).”
Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach (2015, methodology): “, some significant events and policy changes (Yu et al., 2015; Zhang et al., 2008).”
Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, background): “, 2008), and financial time series (Zhang et al., 2008).”
Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling (2020, methodology): “The case studies in [32] and [33] applied EEMD to time series of carbon and crude oil prices and decomposed them into three IMF components: high frequency, low frequency, and trend as shown in Figure 2b.”