Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Yu, Lean; Zhang, Xun; Wang, Shouyang

doi:10.12973/ejmste/77926

Cited by 58 publications

(29 citation statements)

References 37 publications

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“…SVM is a type of learning algorithm proposed to improve the neural network's generalizability to achieve the global optimum solutions. SVM was first introduced for classification tasks, and it is extended for regression and time-series forecasting problems with excellent outcomes [47,109].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…SVM has been applied in various domains for price forecasting, such as crude oil [44][45][46][47], rubber [48], gold [49], and electric [50][51][52][53], agricultural [54], and stock [55] from 2005 to 2021. SVM can avoid the over-fitting problem and model the nonlinear relationships stably, as it applied the risk minimization principle in training.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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“…One such technique, which has been gaining high popularity in recent years, is support vector regression (SVR). In particular, SVR and SVR-based hybrid models have been applied in many studies to forecast prices of energy commodities, including: crude oil (e.g., [13][14][15][16][17][18]) and natural gas (e.g., [19,20]). However, they have been used only by Zhang and Zhang [8] to forecast volatility of energy commodities, specifically crude oil.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

2020

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We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

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“…To cope with the complex characteristics of crude oil prices, more and more scholars use AI models to forecast crude oil prices. The most popular AI models include artificial neural network (ANN) [12,[24][25][26][27], support vector regression (SVR) [28,29] and least squares SVR (LSSVR) [2,10,30], sparse Bayesian learning (SBL) [31,32], extreme learning machine (ELM) [23,33,34], extreme gradient boosting (XGBoost) [8], random vector functional link (RVFL) network [11], long short-term memory (LSTM) [35], and so on. Yu et al used a feed-forward neural network (FNN) to forecast each decomposed component from the raw series of crude oil prices, and then integrated the results of all components as the final forecasting result by an adaptive linear neural network (ALNN) [12].…”

Section: Introductionmentioning

confidence: 99%

“…Barunik and Malinska found that a focused time-delay NN could achieve higher accuracy than the compared models when forecasting monthly crude oil prices [26]. Extensive research has demonstrated that the kernel-based methods, such as SVR, LSSVR and relevance vector machine, are promising for forecasting crude oil prices [2,10,[28][29][30]36]. Very recently, the LSTM, an artificial recurrent NN architecture widely used in deep learning, has been applied to forecasting crude oil prices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Zhou

et al. 2019

Energies

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As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

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Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Cited by 58 publications

References 37 publications

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

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