Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning

Li, Taiyong; Qian, Zijie; Deng, Wu; Zhang, Duzhong; Lu, Haijun; Wang, Shuheng

doi:10.1016/j.asoc.2021.108032

Cited by 80 publications

(34 citation statements)

References 59 publications

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“…Investor complexities could be revealed through the application of decomposed time series so that they eliminate weak signals, leaving behind true signals only [19]. e relevancy of decomposition has been proven in several studies, inter alia, as a means of overcoming complexities-such as nonstationarity and asymmetries-in financial data [5,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…We count the following significant contributions by our study. First, our decomposition method, the ICEEMDAN, corrects the flaws of subjective wavelet decomposition, which is seldom employed to investigate correlations across time scales [22,23,25]. e decomposition approach also overcomes the shortfalls of the VMD and the earlier versions of the EMD approaches.…”

Section: Introductionmentioning

confidence: 99%

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Flights‐to‐and‐from‐Quality with Islamic and Conventional Bonds in the COVID‐19 Pandemic Era: ICEEMDAN‐Based Transfer Entropy

et al. 2022

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We revisit the flight-to-quality (FTQ) and flight-from-quality (FFQ) occurrences vis-à-vis the stock-bond nexus across differing investment time scales in the COVID-19 era, using a novel technique hinged on a denoised frequency-domain transfer entropy. Our findings divulge that flights, both FTQ and FFQ, could be attained during stress periods. Generally, in the intermediate term of the COVID-19 pandemic, both Islamic and conventional bonds could act as safe havens, diversifiers, and hedges for international equities, and the same could be observed for international equities. We reiterate empirically that flights may improve the financial system’s stability and robustness by allowing diversity to be effective when it is most required. The findings have financial and portfolio implications for investors considering how to deploy their investments in the COVID-19 era. Our findings may impact policymakers’ responses to changes in various asset classes, allowing them to better monitor financial markets and adjust macroeconomic policies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flights‐to‐and‐from‐Quality with Islamic and Conventional Bonds in the COVID‐19 Pandemic Era: ICEEMDAN‐Based Transfer Entropy

et al. 2022

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“…A chaotic ant colony optimization (CACO) algorithm is a combination of chaos search and the ant colony optimization algorithm. After the ant colony search is completed, chaos is used to improve the search accuracy and avoid falling into the local optimum [28][29][30][31][32][33][34][35][36][37][38][39][40]. Therefore, the CACO algorithm takes on a global optimization ability.…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

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Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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“…The application of big data has served as a basic strategic digital resource in smart cities. Many researchers have analyzed the trajectory GPS data of transportation vehicles in order to mine the hidden information behind the data to reflect the urban operation status and define temporal and spatial change rules [1], in addition to use in traffic congestion status analysis [2][3][4][5][6][7], crowd movement distribution [8][9][10], traffic travel recommendation [11,12], and road planning [13,14], urban hotspot discovery [15][16][17][18], and so on. Such research results are directly applied to the construction of a smart city to elucidate more reasonable urban road planning and a more reasonable dispersion of vehicle flow and human flow.…”

Section: Introductionmentioning

confidence: 99%

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

et al. 2021

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With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.

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Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning

Cited by 80 publications

References 59 publications

Flights‐to‐and‐from‐Quality with Islamic and Conventional Bonds in the COVID‐19 Pandemic Era: ICEEMDAN‐Based Transfer Entropy

Flights‐to‐and‐from‐Quality with Islamic and Conventional Bonds in the COVID‐19 Pandemic Era: ICEEMDAN‐Based Transfer Entropy

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

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