Forecasting Crude Oil Prices with a WT-FNN Model

Wang, Donghua; Fang, Tianhui

doi:10.3390/en15061955

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…In the literature, many publications can be found related to forecasting the demand for fossil resources, but only a small part of the articles concern forecasting the demand for crude oil. There are many ways of forecasting the demand for energy resources, autoregressive and moving average (ARMA) models [84,85], generalized ARCH model [86], models of the stochastic effective function [87], and methods of forecasting time series through artificial neural networks [88][89][90]. Table 3 summarizes existing research on fossil fuel consumption forecasting.…”

Section: Methodsmentioning

confidence: 99%

Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network

Manowska

Bluszcz

2022

Energies

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Primary fuels, i.e., crude oil, natural gas, and power coal, dominate the total global demand for primary energy. Among them, crude oil plays a particularly important role due to the universality of applications and the practical lack of substitutes in transport. Crude oil is also one of the main sources of primary energy in Poland and accounts for around 30% of the energy consumed. Poland covers only 3% of its needs from domestic deposits. The rest is imported from Russia, Saudi Arabia, Nigeria, Great Britain, Kazakhstan, and Norway. Due to such a high import of raw material, Poland must anticipate future demand. On the one hand, this article aims to analyze the current (2020) and future (2040) crude oil consumption on the Polish market. The study analyzes the geopolitical and economic foundations of the functioning of the energy raw-materials market, the crude oil supply, the structure of Poland’s energy mix, and assumptions about the energy policy until 2040. On the other hand, conclusions from the research were used to build a model of crude oil consumption for the internal market. It has been also shown that the consumption of crude oil on the Polish market is a nonlinear phenomenon with a small set of statistical data, which makes it difficult to build an accurate model. This paper proposes a new model based on artificial neural networks that includes long-term memory (LSTM). The accuracy of the constructed model was assessed using the MSE, Theil, and Janus coefficients. The results show that LSTM models can be used to forecast crude oil consumption, and they cope with the nonstationary and nonlinear time series. Many important contemporary problems posed in the field of energy economy are also discussed, and it is proposed to solve them with the use of modern machine-learning tools.

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Section: Methodsmentioning

confidence: 99%

Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network

Manowska

Bluszcz

2022

Energies

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“…The results showed that the sentimentbased machine learning models outperformed traditional time-series models like ARIMA and GARCH, indicating the effectiveness of using investor sentiment and machine learning for crude oil futures price prediction. Wang and Fang proposed a method for crude oil price forecasting called WT-FNN, which uses a wavelet transform-based fuzzy neural network (Wang and Fang, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Particle Swarm Optimization-Gravitational Search Algorithms Deep Learning Networks to Simultaneously Project Multiple Crude Oil Price

Isaac,

Thomas Lass,

Idris

et al. 2023

J. tech. innov.

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The conventional linear econometric and statistical models are not effective for forecasting the nonlinear and complex nature of crude oil prices. Computational intelligence techniques and hybrid modelling principles have been proposed to address this issue. Multiple forecasts can be combined using linear or nonlinear methods to create an aggregate forecast. Currently, there is no study on the optimization of deep learning with a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO) for forecasting crude oil price benchmarks, including WTI, Brent, and Dubai. Additionally, most studies have focused only on using West Texas Intermediate (WTI) crude oil spot prices as their benchmark. A Bidirectional Long Short-Term Memory with hybrid gravitational search algorithm (GSA) and particle swarm optimization (PSO) to forecast Crude Oil prices is proposed. The proposed model outperformed FNNPSOGSA, FNNGA, LSTM and BiLSTM models with root mean square errors (RMSE) of 0.0029, 0.0011, and 0.0029, respectively. The proposed model is suited for Crude Oil forecasting.

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“…In-sample and out-of-sample performance comparisons revealed that the s-PCA model was superior to the compared models. Wang & Fang [30] developed a novel combination of the FNN model and stochastic time effective function for crude oil prices forecasting, i.e., the WT-FNN model, and the findings revealed that the WT-FNN model had the best predictive impact. Zhang et al [15] offered a novel hybrid technique to predict crude oil prices based on the least square support vector machine, particle swarm optimization, and GARCH model.…”

Section: Forecasting By Hybrid Modelsmentioning

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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Forecasting Crude Oil Prices with a WT-FNN Model

Cited by 10 publications

References 44 publications

Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network

Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network

Hybrid Particle Swarm Optimization-Gravitational Search Algorithms Deep Learning Networks to Simultaneously Project Multiple Crude Oil Price

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

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