A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Zhou, Jianguo; Wang, Shiguo

doi:10.3390/en14051328

Cited by 40 publications

(19 citation statements)

References 35 publications

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“…Such drawbacks can be handled by optimizing the initial connection weight and threshold. Therefore, many scholars use intelligent optimization algorithms to select the initial connection weight and threshold value of neural networks, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Grey Wolf Optimizer (GWO), and SSA [ 32 – 36 ]. In this study, SSA was used to optimize the initial connection weight and threshold of the BP neural network, and the optimal connection weight and threshold found by SSA were given to the BP neural network to establish the optimal BP neural network model.…”

Section: Ssa-bp Neural Networkmentioning

confidence: 99%

Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network

Luo

Zhao

Chen

2022

Computational Intelligence and Neuroscience

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Due to the marked increase in the prevalence of overweight and obesity worldwide and an environment leading to a series of chronic diseases, physical exercise is an important way to prevent chronic diseases. Additionally, a good exercise smart bracelet can bring convenience to physical exercise. Quick and accurate evaluation of smart sports bracelets has become a hot topic and draws attention from both academic researchers and public society. In the literature, the analytic hierarchy process (AHP) and entropy weight method (EWM) were used to obtain the weights from both subjective and objective perspectives, which were integrated by the comprehensive weighting method, and furthermore the performance of sports smart bracelet was evaluated through fuzzy comprehensive evaluation. Also, to avoid complex weight calculations caused by the comprehensive weighting method, machine learning methods are used to model the structure and contribute to the comprehensive evaluation process. However, few studies have investigated all previous elements in the comprehensive evaluation process. In this study, we consider all previous parts when evaluating smart sports bracelets. In particular, we use the sparrow search algorithm (SSA) to optimize the backpropagation (BP) neural network for constructing the comprehensive score prediction model of the sports smart bracelet. Results show that the sparrow search algorithm-optimized backpropagation (SSA-BP) neural network model has good predictive ability and can quickly obtain evaluation results on the premise of effectively ensuring the accuracy of the evaluation results.

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Section: Ssa-bp Neural Networkmentioning

confidence: 99%

Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network

Luo

Zhao

Chen

2022

Computational Intelligence and Neuroscience

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“…The finite distributed lag (FDL) model based on a genetic algorithm (GA) has better performance on predicting carbon price than other GARCH models [ 22 ]. Based on the idea of ensemble learning, the EMD model (Empirical Mode Decomposition, EMD) is used to extract the intrinsic mode function (IMF) that represents the different coexisting oscillation modes of carbon series [ 23 , 24 , 25 ], and then a hybrid carbon price forecasting model integrating the variational mode decomposition (VMD) and optimal combination forecasting model (CFM) is constructed, the results suggesting the superiority of the proposed hybrid model for carbon price forecasting [ 26 , 27 ]. Conducting the EMD method, Wang et al [ 28 ] proposed a new random forest-based nonlinear ensemble paradigm for carbon price prediction and proved the model’s superiority in European carbon price forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Yun

Zhang

et al. 2022

IJERPH

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The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

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“…Sparrow search algorithm (SSA) is an emerging evolutionary algorithm based on the sparrow's foraging and anti-predation behaviors (Zhou & Wang 2021). Compared to several traditional evolutionary algorithms, SSA has stronger global search ability and faster convergence speed in global optimization problems.…”

Section: Sparrow Search Algorithm (Ssa)mentioning

confidence: 99%

Hydrological time series prediction by extreme learning machine and sparrow search algorithm

Feng

Zou

et al. 2021

Water Supply

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In general, accurate hydrological time series prediction information is of great significance for the rational planning and management of water resource system. Extreme learning machine (ELM) is an effective tool proposed for the single-layer feedforward neural network in the regression and classification problems. However, the standard ELM model falls into local minimum with a high probability in hydrological prediction problems since the randomly assigned parameters (like input-hidden weights and hidden biases) often remain unchanged at the learning process. For effectively improving the prediction accuracy, this paper develops a hybrid hydrological forecasting model where the emerging sparrow search algorithm (SSA) is firstly used to determine the satisfying parameter combinations of the ELM model, and then the Moore-Penrose generalized inverse method is chosen to analytically obtain the weight matrix between the hidden layer and output layer. The proposed method is used to forecast the long-term daily runoff series collected from three real-world hydrological stations in China. Based on several performance evaluation indexes, the results show that the proposed method outperforms several ELM variants optimized by other evolutionary algorithms in both training and testing phases. Hence, an effective evolutionary machine learning tool is developed for accurate hydrological time series forecasting. HIGHLIGHT Hydrologic forecasting, sparrow search algorithm, extreme machine learning.

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A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Cited by 40 publications

References 35 publications

Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network

Research on User Experience of Sports Smart Bracelet Based on Fuzzy Comprehensive Appraisal and SSA-BP Neural Network

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Hydrological time series prediction by extreme learning machine and sparrow search algorithm

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