A memory-trait-driven decomposition–reconstruction–ensemble​ learning paradigm for oil price forecasting

Yu, Lean; Ma, Mengyao

doi:10.1016/j.asoc.2021.107699

Cited by 20 publications

(1 citation statement)

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“…Yu et al [11] first proposed a decomposition-ensemble model with a reconstruction step that considered some data characteristics. Recently, Yu & Ma [12] introduced a memory-trait-driven reconstruction method into the decomposition and ensemble framework. Inspired by their work, a new model based on decomposition-ensemble learning with a reconstruction step that considers the data complexity traits is used to explore the price predictions of crude oil.…”

Section: Introductionmentioning

confidence: 99%

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits

Wang

2023

Entropy

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The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition–reconstruction–ensemble for crude oil price forecasting is proposed. Based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, in this paper we construct a recursive CEEMDAN decomposition–reconstruction–ensemble model considering the complexity traits of crude oil data. In this model, the steps of mode reconstruction, component prediction, and ensemble prediction are driven by complexity traits. For illustration and verification purposes, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Thus, the proposed recursive CEEMDAN decomposition–reconstruction–ensemble model can be an effective tool to forecast oil price in the future.

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Section: Introductionmentioning

confidence: 99%

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits

Wang

2023

Entropy

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A novel stock indices hybrid forecasting system based on features extraction and multi-objective optimizer

Wang

2022

Appl Intell

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Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price

Saghi

Rezaee

2021

Comput Econ

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In this paper, hybrid methods are proposed to predict OPEC crude oil. In the preprocessing step, the wavelet decomposition has been used to reduce the noise of time series, which divides the original data into five levels. Also, the fuzzy transform (F-transform) is applied for its potency of management uncertainty due to the fluctuation of data. F-transform is for decomposing the time series data and afterward, this decomposed data are employed on the ground of inputs. Because of the nonlinearity of the time series structure, integrating the mentioned proposed algorithms with the multilayer perceptron (MLP), radial basis functions, group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) are applied for prediction of time series relevant to the crude oil price. By comparing the results of models, L5-ANFIS, L5-MLP, L5-GMDH, L5-SVR are more appropriate than others for predicting OPEC crude oil price.

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A memory-trait-driven decomposition–reconstruction–ensemble learning paradigm for oil price forecasting

Cited by 20 publications

References 50 publications

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits

A novel stock indices hybrid forecasting system based on features extraction and multi-objective optimizer

Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price

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A memory-trait-driven decomposition–reconstruction–ensemble​ learning paradigm for oil price forecasting

Cited by 20 publications

References 50 publications

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits

A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits

A novel stock indices hybrid forecasting system based on features extraction and multi-objective optimizer

Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price

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A memory-trait-driven decomposition–reconstruction–ensemble learning paradigm for oil price forecasting