A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price

Xu, Wei; Wang, Jue; Zhang, Xun; Zhang, Wen; Wang, Shouyang

doi:10.1007/978-3-540-72588-6_154

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Xu et al [44] employed text mining techniques to evaluate the related factors affecting oil prices and found that the world's total demand and supply are the most crucial factors, while demand is dominant. Furthermore, they also found that world economic growth and exchange rates affect crude oil prices as well and increase their volatility.…”

Section: Sentiment Analysis Application and Resultsmentioning

confidence: 99%

“…Xu et al [44] utilized text mining techniques that involved TF-IDF metrics and a K-mixture model to select attributes from a document collection. Therefore, in order to reduce the number of attributes selected, the authors applied the rough sets theory, whose main assumption is that every object in the universe of discourse is associated with some information.…”

Section: Sentiment Analysis Techniquesmentioning

confidence: 99%

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Oil Sector and Sentiment Analysis—A Review

2023

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Oil markets reveal considerably volatile behaviour due to a range of factors. Exogenous factors, such as the COVID-19 pandemic and ongoing wars and conflicts, impose even more difficulties for prediction purposes. As a tool to better understand and improve forecasting models, many researchers are using sentiment analysis techniques to identify the sentiments being emanated in the news and on social media. Following the PRISMA standards, this work systematically reviewed 34 studies out of 320 from the Scopus and Web of Science databases. The results indicate that one can use several different sources to construct a text dataset and develop a sentiment analysis. For instance, Reuters, Oilprice.com, and Twitter are among the more popular ones. Among the approaches used for extracting public sentiment, it became apparent that machine learning-based methods have been increasing in prevalence in recent years, both when applied alone and in conjunction with lexicon-based methods. Finally, regarding the purpose of employing sentiment analysis, the most favourable goal for collecting sentiments concerning the oil market is to forecast oil prices. There is a consensus among the authors that sentiment analysis improves the quality of predictive models, making them more accurate. This work aims to assist academics, researchers, and investors interested in the oil sector.

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Section: Sentiment Analysis Application and Resultsmentioning

confidence: 99%

Section: Sentiment Analysis Techniquesmentioning

confidence: 99%

Oil Sector and Sentiment Analysis—A Review

2023

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“…Xu et al [94] proposed a rough set and wavelet neural networks (RSWNN) hybrid model for analyzing the factors that affected crude oil prices and predict future prices. Their approach consisted of text mining, rough set and wavelet neural networks.…”

Section: Applications Of Hybridized and Single CI Techniques In Crudementioning

confidence: 99%

Computational Intelligence Techniques With Application to Crude Oil Price Projection: A Literature Survey From 2001–2012

Chiroma¹,

Kareem²,

Abubakar³

et al. 2013

NNW

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Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

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Forecasting Crude Oil Prices: A Machine Learning Perspective

Purohit,

Panigrahi

2024

Communications in Computer and Information Science

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A New Hybrid Approach for Analysis of Factors Affecting Crude Oil Price

Cited by 5 publications

References 14 publications

Oil Sector and Sentiment Analysis—A Review

Oil Sector and Sentiment Analysis—A Review

Computational Intelligence Techniques With Application to Crude Oil Price Projection: A Literature Survey From 2001–2012

Forecasting Crude Oil Prices: A Machine Learning Perspective

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