Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

Jianwei, E; Bao, Yufang; Ye, Jimin

doi:10.1016/j.physa.2017.04.160

Cited by 89 publications

(7 citation statements)

References 32 publications

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“…Structural decomposition analysis (SDA) and quantile regression have been applied to investigate changes in the carbon emission intensity in China 8 . A hybrid method combining variational mode decomposition (VMD), independent component analysis (ICA), and autoregressive integrated moving average (ARIMA) has been proposed to analyze the influencing factors of crude oil prices and to predict the future crude oil prices 9 . The fuzzy analytic hierarchy process has been used to analyze the e advantages and disadvantages of various renewable energy options in Turkey 10 , and results have indicated that wind energy is the best choice, followed by solar energy and biomass energy.…”

mentioning

confidence: 99%

Regression analysis and driving force model building of CO2 emissions in China

Zhou

Zhang

2021

Sci Rep

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In recent years, global warming has become increasingly devastating, leading to severe consequences, such as extreme weather events and sea-level rise. To reduce carbon dioxide emissions, it is essential to recognize different emission sources and key driving factors. Three main carbon emission sources from the period between 1990 and 2017 were identified in China: the energy industry, fuel combustion in other industries, and industrial process. For each source, a driving force model was developed via multiple linear regression. Based on these models, forecasts of the carbon intensity and total CO2 emissions were obtained from 2018 to 2030. The results demonstrate that the CO2 emission intensity and total emissions will continue to decrease but more effort will be required to achieve the goal of Paris Agreement.

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confidence: 99%

Regression analysis and driving force model building of CO2 emissions in China

Zhou

Zhang

2021

Sci Rep

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“…For a more detailed theory of kernel functions and the evolution of SVM, the reader is directed to the text book on support vector machines by Soman et al [27]. Based on…”

Section: Principle Analysis Of Svm Methodsmentioning

confidence: 99%

“…VMD was used in bearing fault analysis in [21][22][23] and the spreading in the frequency of the signal and intensity of vibration was demonstrated as a clear indication of a fault in bearings. VMD was also used in real-time power signal decomposition [24], brain magnetic resonance [25], ECG feature extraction and classification [26] and more recently in crude oil price analysis and forecasting [27]. VMD has also found applications in speech processing for the detection of voiced and non-voiced segments [28], instantaneous fundamental frequency [29] and estimation of glottal closure and opening instants in EGG [30].…”

Section: Introductionmentioning

confidence: 99%

Mode determination in variational mode decomposition and its application in fault diagnosis of rolling element bearings

2019

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The non-stationary nature of bearing vibrations makes it difficult to extract features from the real-time signature of faulty rolling element bearings (REBs). The present work suggests to improve the diagnostic accuracy of fault detection in REBs by enhancing the mode selection property of variational mode decomposition, manipulating its initialization and input parameters (bandwidth selection parameter, and the number of modes, k ) and then extracting energy entropy features and using cross-validation in support vector machine (SVM) classifier. The alpha values and the number of modes are varied from (100, 1000000) and (4, 10), respectively. Mean absolute error (MAE) is used as the indicator which calculates error values obtained between the respective sum of modes and the original signal. The particular − k combination with the least MAE value is chosen. The above method is tested using REB signals obtained from the bearing prognostics test rig. The results obtained from the proposed approach shows 100% diagnostic accuracy in detecting faulty REB vibration signature using five-fold cross-validation in SVM classifier.

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“…Some subseries reflect short-term fluctuations, while others depict the long-term trends inherent in the original series [48]. Various decomposition methods can be employed to categorize the signal-decomposition model, including the wavelet method [49], empirical mode decomposition [26], ensemble empirical mode decomposition [26,50], seasonal adjustment methods [51] and many various decomposition methods [48,52]. The authors in [53] used the type of autocorrelation integrated with Q-learning Swarm Optimization for finding the possible interaction amid of irrigation points with the credentials of high-impact irrigation zones.…”

Section: Signal Decompositionmentioning

confidence: 99%

A Novel Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting of Crude Oil Prices

Naeem,

Aamir,

et al. 2024

IEEE Access

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In recent eras, the complexity and fluctuations of the global crude oil prices have affected the economic progress of society. It is therefore, the oil price prediction has hauled the attention of scholars and policymakers. Driven by this critical concern for forecasting of crude oil prices, we introduces a novel hybrid model keeping in mind the primary objective of enhancing prediction accuracy while considering the specific characteristics as inherent in the data. To achieve this achievement, the trend is eliminated, allowing the scrutiny of whether the residual component validates the assurance of a series ran by stochastic trends. Following the removal of the trend, the residual component undergoes rigorous evaluation through autoregressive model following the decomposition model. Then we got support from the vector machine, autoregressive integrated moving average and long-short term memory. The predictions accuracy can be evaluated by using the various performance metrics. The proposed hybrid model's robustness and forecasting performance are rigorously evaluated through Diebold-Mariano test in comparison to competing models. Furthermore, the forecasting ability is evaluated via directional forecast. Ultimately, the empirical findings explicitly determine the superior predictive capabilities of the proposed hybrid model over alternative approaches.INDEX TERMS Crude Oil Prices, Decomposition and Ensemble Model, Forecasting, Reconstructions of IMFs.

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Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

Cited by 89 publications

References 32 publications

Regression analysis and driving force model building of CO2 emissions in China

Regression analysis and driving force model building of CO2 emissions in China

Mode determination in variational mode decomposition and its application in fault diagnosis of rolling element bearings

A Novel Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting of Crude Oil Prices

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