A Novel Hybrid Carbon Price Forecasting Model Based on Radial Basis Function Neural Network

Wang, S.; Li, S.

doi:10.12693/aphyspola.135.368

Cited by 16 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many forms of RBF, many of which satisfy the Micchelli theorem, and its characteristic is that the quantity of hidden nodes is equal to the quantity of input samples. The following three RBFs satisfy the Micchelli theorem [ 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Zhu

Meng

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

show abstract

Section: Methodsmentioning

confidence: 99%

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Zhu

Meng

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Furthermore, in a series of other developed decomposition algorithms developed, by adopting an adaptive decomposition mode for the effective components of each center frequency, VMD can capture the characteristics of data more effectively by adopting an adaptive decomposition mode for the effective components of each center frequency (da Silva et al, 2022). Its advantages have been confirmed in the existing research on carbon price prediction (Wang et al, 2019;Zhu et al, 2019;Sun and Huang, 2020).…”

Section: Introductionmentioning

confidence: 87%

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

Zhang

Tang

2023

Front. Energy Res.

View full text Add to dashboard Cite

Numerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for higher accuracy of the carbon price series prediction and following the existing research path, this paper proposes a new hybrid prediction model VMD-CEEMDAN-LSSVM-LSTM, which combines a new quadratic decomposition technique with the optimized long short term memory (LSTM). In the decomposition part of the hybrid model, the original carbon price series is processed by variational mode decomposition (VMD), and then the residual term obtained by decomposition is further decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the prediction part of the hybrid model, least squares support vector machine (LSSVM) is introduced, and LSSVM-LSTM model is constructed to predict the components obtained by decomposition. The empirical research of this paper selects two different case data from the European Union emissions trading system (EU ETS) as samples. Taking the results of Case Ⅰ in the 1-step ahead forecasting scenario as an example, the prediction evaluation indexes eMAPE, eRMSE and R2 of the VMD-CEEMDAN-LSSVM-LSTM hybrid model constructed in this paper are 0.3087, 0.0921 and 0.9987 respectively, which are significantly better than other benchmark models. The empirical results confirm the superiority and robustness of the hybrid model proposed in this paper for carbon price forecasting.

show abstract

“…Three forms of RBF that can satisfy Micchelli's theorem are given below (Gaussian, Multiquadric, and Inverse polyquadratic function) (equations 5-7). The special feature of each of these RBFs is that the amount of input samples is equal to the number of hidden nodes (Wang and Li, 2019).…”

Section: Rbfnnmentioning

confidence: 99%

A new development algorithm for permeability prediction: A new milestone

Sun

Liang

2022

Front. Ecol. Evol.

View full text Add to dashboard Cite

Permeability is one of the most important reservoir rock parameters in petroleum engineering, reservoir, and exploitation. This parameter causes the movement of hydrocarbon reserves in the reservoir rock. Therefore, it is an important parameter from the economic point of view because it greatly impacts the amount of extraction from the reservoir rock. In this study, the combined RBFNN-GA algorithm and 200 data sets collected from a field in the Middle East were used to predict permeability. Water saturation, porosity, and specific surface are the input variables used in this study. GA has advantages such as solving complex optimization problems of continuous functions and multi-objective problems. The advantages of RBF neural networks are that they are easy to design, strongly tolerant to input noise, and have good generalization. The RBFNN-GA model has the advantages of both algorithms. RBFNN-GA algorithm and experimental models have been compared in terms of performance accuracy. The results show that RBFNN-GA with STD = 89.8 and R-square = 0.9011 for the total data set obtained from a field in the Middle East has better accuracy and performance in predicting permeability than experimental models. Compared to other neural network methods, the RBFNN-GA model has a higher performance accuracy and is efficient for predicting other parameters. Oil researchers and engineers can use this method to predict other parameters in their studies and research.

show abstract

A Novel Hybrid Carbon Price Forecasting Model Based on Radial Basis Function Neural Network

Cited by 16 publications

References 19 publications

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach

A new development algorithm for permeability prediction: A new milestone

Contact Info

Product

Resources

About