Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

By Lean Yu, Shouyang Wang, Kin Keung Lai. 2008

709 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/213d6146f4a29d2d566ba7c086e4ec55f311a683

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In Analytical Chemistry

In Control and Systems Engineering

In Economics and Econometrics

Citations in the corpus

Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM (2020, methodology): “[53] used the EMD method to determine the main driving power behind the volatility of oil prices and showed that irregular events increase volatility.”
Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model (2012, methodology): “Motivated by the thought of [19], the study in this paper develops a modi ﬁ ed model, which is modeled through improving the EMD-based arti ﬁ cial neural network in order to remedy the above shortcomings, for wind speed forecasting.”
Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach (2015, background): “, short-termmarket fluctuations, significant breaks with medium-term effects, and long-term trend (Zhang et al., 2008; Yu et al., 2008; He et al., 2012; Zhu, 2012; Zhu et al., 2015; Yu et al., 2014).”
Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold (2013, background): “With both the increasing integration and high level of volatility of major financial markets, commodity behavior and equity prices grew more sensitive to innovations, such as deregulation, weather, war, political economic, investors’ psychological expectations (Yu et al., 2008), revolutions and unfo”
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015, methodology): “[20] Fine-to-coarse (FTC) reconstruction Mean Zhang et al.”