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学者姓名：张婷婷
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An accurate prediction of carbon pricing is essential in carbon emission management, and also provides an important role for governments to formulate corresponding policies. However, due to the inherent complexity and dynamics of carbon price sequence, the effectiveness of different decomposition algorithms for carbon price remains to be tested. In addition, existing studies lack a systematic framework to explore the organic integration of external factors and secondary decomposition technology, and the feature processing of complex external factors still needs to be improved. In order to overcome the shortcomings of existing research, This paper presents a Variational Modal Decomposition(VMD) algorithm and a Complete Ensemble Empirical Mode Decomposition with Adaptive Second decomposition technology of Noise(CEEMDAN) decomposition algorithm, and extract the features of external factors by Extreme Gradient Boosting (XGBoost) algorithm. The HI-VMD-PE-CEEMDAN-XGBoost-Transformer model for predicting carbon price is constructed by the combined Transformer algorithm. Specifically, first, we use Hampel identifer(HI) to detect and rectify the anomalies in the original sequence. After applying Variational Mode Decomposition(VMD) decomposition algorithm, Permutation Entropy(PE) is utilized to reassemble the decomposed component. Quadratic Decomposition is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) algorithm. Then, the XGBoost algorithm is employed to extract features of external factors and screen key factors as predictive input variables. Finally, Transformer, which has stronger capability of large-scale data parallel processing, is selected as the prediction model to achieve a more scientific and effective carbon price prediction. The empirical analysis results based on EU carbon market data verify the validity and superiority of the proposed model in different forecasting scenarios.
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| GB/T 7714 | Zheng, Hairong , Zhuang, Sikai , Zhang, Tingting . Research on forward multi-step prediction of EU carbon prices considering multiple factors new evidence from a hybrid model combining secondary decomposition technique and transformer [J]. | PLOS ONE , 2025 , 20 (6) . |
| MLA | Zheng, Hairong 等. "Research on forward multi-step prediction of EU carbon prices considering multiple factors new evidence from a hybrid model combining secondary decomposition technique and transformer" . | PLOS ONE 20 . 6 (2025) . |
| APA | Zheng, Hairong , Zhuang, Sikai , Zhang, Tingting . Research on forward multi-step prediction of EU carbon prices considering multiple factors new evidence from a hybrid model combining secondary decomposition technique and transformer . | PLOS ONE , 2025 , 20 (6) . |
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Decoding brain activity from non-invasive motor imagery electroencephalography (MI-EEG) signals is vital for brain-computer interface (BCI). Many studies overlook the feature-classifier interaction, impacting decoding performance. To tackle this issue, we propose a decoding framework for MI-EEG signals that uses multi-view weighted features. Our approach begins by employing a multi-view feature fusion mechanism to capture both global and local features from raw MI-EEG signals. Following feature extraction and fusion, we utilize the Expectation-Maximization (EM) algorithm to partition the samples to distinct soft subspaces. The subset information is subsequently used to classify uncategorized MI-EEG samples. Within this framework, features that are more relevant to the subsequent classification model are assigned greater weights. Results from public benchmark datasets demonstrate that, compared to commonly used models, our method achieves superior classification results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Benchmarking Benchmarking Biomedical signal processing Biomedical signal processing Brain mapping Brain mapping Electroencephalography Electroencephalography Expectation maximization algorithm Expectation maximization algorithm
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| GB/T 7714 | Li, Nan , Li, Wangsen , Zhang, Tingting et al. Motor Imagery EEG Signals Decoding with Multi-view Weighted Features [C] . 2025 : 338-349 . |
| MLA | Li, Nan et al. "Motor Imagery EEG Signals Decoding with Multi-view Weighted Features" . (2025) : 338-349 . |
| APA | Li, Nan , Li, Wangsen , Zhang, Tingting , Huang, Dong , Han, Junfeng , Kong, Xiangzeng . Motor Imagery EEG Signals Decoding with Multi-view Weighted Features . (2025) : 338-349 . |
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This study investigates the risk contagion characteristics between the green bonds (GBs) market and financial markets, with particular attention to the impact of economic policy uncertainty (EPU) and geopolitical risk (GPR). Given the limitations of traditional models in accounting for multiple sources of uncertainty and capturing nonlinear risk dependencies across diverse markets, this paper proposes a novel GARCH-MIDAS-D-Vine-Copula-CoVaR framework to comprehensively analyze the dynamic risk spillovers between the GBs market and heterogeneous financial sub-markets, including equities, bonds, and Bitcoin. The results show three main findings. First, when incorporating uncertainty factors, the risk spillovers between the GBs market and financial sub-markets exhibit notable heterogeneity: the stock market amplifies risk spillovers from GBs, the bond market serves as a buffer, while the effect of the Bitcoin market is relatively limited. Second, when considering the dynamic interactions among the three financial sub-markets jointly, the overall risk spillovers between the GBs market and the financial system tend to ease. Third, under major shocks, the risk spillovers between GBs and financial markets were significantly higher during the COVID-19 outbreak than during the Russia-Ukraine conflict, though the latter exhibited longer persistence and greater frequency of fluctuations. These findings provide valuable insights for environmentally conscious investors and regulators, enabling them to make investment and regulatory decisions based on the distinct characteristics of risk spillovers between GBs and various financial markets.
Keyword ：
Financial market Financial market Green bond Green bond Risk contagion Risk contagion Uncertainties Uncertainties
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| GB/T 7714 | Zheng, Hairong , Wang, Sikai , Zhang, Tingting . Dynamic risk spillovers between green bond markets and financial markets: Novel perspective integrating uncertainty factors and multivariate Copula methods [J]. | JOURNAL OF CLEANER PRODUCTION , 2025 , 533 . |
| MLA | Zheng, Hairong et al. "Dynamic risk spillovers between green bond markets and financial markets: Novel perspective integrating uncertainty factors and multivariate Copula methods" . | JOURNAL OF CLEANER PRODUCTION 533 (2025) . |
| APA | Zheng, Hairong , Wang, Sikai , Zhang, Tingting . Dynamic risk spillovers between green bond markets and financial markets: Novel perspective integrating uncertainty factors and multivariate Copula methods . | JOURNAL OF CLEANER PRODUCTION , 2025 , 533 . |
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The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMD-SGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research.
Keyword ：
agricultural futures agricultural futures long short-term memory model long short-term memory model price forecast price forecast symplectic geometry mode decomposition symplectic geometry mode decomposition variational mode decomposition variational mode decomposition
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| GB/T 7714 | Zhang, Tingting , Tang, Zhenpeng . Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model [J]. | FRONTIERS IN SUSTAINABLE FOOD SYSTEMS , 2024 , 8 . |
| MLA | Zhang, Tingting et al. "Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model" . | FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 8 (2024) . |
| APA | Zhang, Tingting , Tang, Zhenpeng . Agricultural commodity futures prices prediction based on a new hybrid forecasting model combining quadratic decomposition technology and LSTM model . | FRONTIERS IN SUSTAINABLE FOOD SYSTEMS , 2024 , 8 . |
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The continuous rise in global economic policy uncertainty (EPU) and geopolitical risk (GPR) has intensified market volatility, altered investor preferences, impacted capital flows, and complicating the risk spillover between green bonds and energy markets. Existing research has not adequately addressed the impact of different uncertainties on risk contagion or analyzed the risk contagion characteristics between sub-markets. Therefore, this study first establishes a GARCH-MIDAS model that simultaneously considers EPU and GPR. Secondly, it breaks through the traditional binary risk research framework by employing a D-Copula model to characterize the nonlinear dependence between green bonds and various sub-markets. To achieve this, a new GARCH-MIDASD-Copula- CoVaR model was developed to dynamically describe the characteristics of risk spillover.The results show that the impact of different energy sub-markets on green bonds varies significantly, with the overall energy market exhibiting weaker risk spillover effects compared to individual sub-markets. Focusing on the outbreak of the COVID-19 pandemic, this study reveals the risk spillover characteristics between green bonds and energy markets during different periods, providing a new perspective for studying the risk spillover relationship between the two markets.
Keyword ：
Energy market Energy market Green bond Green bond Risk contagion Risk contagion Uncertainties Uncertainties
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| GB/T 7714 | Zheng, Hairong , Wang, Sikai , Zhang, Tingting . Dynamic risk spillovers between green bonds and energy markets: New evidence from the GARCH-MIDAS-D-Copula-CoVaR approach considering uncertainties [J]. | RENEWABLE ENERGY , 2024 , 239 . |
| MLA | Zheng, Hairong et al. "Dynamic risk spillovers between green bonds and energy markets: New evidence from the GARCH-MIDAS-D-Copula-CoVaR approach considering uncertainties" . | RENEWABLE ENERGY 239 (2024) . |
| APA | Zheng, Hairong , Wang, Sikai , Zhang, Tingting . Dynamic risk spillovers between green bonds and energy markets: New evidence from the GARCH-MIDAS-D-Copula-CoVaR approach considering uncertainties . | RENEWABLE ENERGY , 2024 , 239 . |
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Brain–computer interfaces, where motor imagery electroencephalography (EEG) signals are transformed into control commands, offer a promising solution for enhancing the standard of living for disabled individuals. However, the performance of EEG classification has been limited in most studies due to a lack of attention to the complementary information inherent at different temporal scales. Additionally, significant inter-subject variability in sensitivity to biological motion poses another critical challenge in achieving accurate EEG classification in a subject-dependent manner. To address these challenges, we propose a novel machine learning framework combining multi-scale feature fusion, which captures global and local spatial information from different-sized EEG segmentations, and adaptive Lasso-based feature selection, a mechanism for adaptively retaining informative subject-dependent features and discarding irrelevant ones. Experimental results on multiple public benchmark datasets revealed substantial improvements in EEG classification, achieving rates of 81.36%, 75.90%, and 68.30% for the BCIC-IV-2a, SMR-BCI, and OpenBMI datasets, respectively. These results not only surpassed existing methodologies but also underscored the effectiveness of our approach in overcoming specific challenges in EEG classification. Ablation studies further confirmed the efficacy of both the multi-scale feature analysis and adaptive selection mechanisms. This framework marks a significant advancement in the decoding of motor imagery EEG signals, positioning it for practical applications in real-world BCIs. © 2024 by the authors.
Keyword ：
Benchmarking Benchmarking Biomedical signal processing Biomedical signal processing Brain mapping Brain mapping Electroencephalography Electroencephalography Feature Selection Feature Selection Image enhancement Image enhancement Image segmentation Image segmentation Interfaces (computer) Interfaces (computer) Learning aids for disabled persons Learning aids for disabled persons
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| GB/T 7714 | Chen, Shimiao , Li, Nan , Kong, Xiangzeng et al. Electroencephalography-Based Motor Imagery Classification Using Multi-Scale Feature Fusion and Adaptive Lasso [J]. | Big Data and Cognitive Computing , 2024 , 8 (12) . |
| MLA | Chen, Shimiao et al. "Electroencephalography-Based Motor Imagery Classification Using Multi-Scale Feature Fusion and Adaptive Lasso" . | Big Data and Cognitive Computing 8 . 12 (2024) . |
| APA | Chen, Shimiao , Li, Nan , Kong, Xiangzeng , Huang, Dong , Zhang, Tingting . Electroencephalography-Based Motor Imagery Classification Using Multi-Scale Feature Fusion and Adaptive Lasso . | Big Data and Cognitive Computing , 2024 , 8 (12) . |
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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 e M A P E , e R M S E and R 2 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.
Keyword ：
carbon price carbon price LSSVM-LSTM LSSVM-LSTM multi-step ahead forecasting multi-step ahead forecasting quadratic decomposition technique quadratic decomposition technique VMD-CEEMDAN VMD-CEEMDAN
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| GB/T 7714 | Zhang, Tingting , Tang, Zhenpeng . Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach [J]. | FRONTIERS IN ENERGY RESEARCH , 2023 , 10 . |
| MLA | Zhang, Tingting et al. "Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach" . | FRONTIERS IN ENERGY RESEARCH 10 (2023) . |
| APA | Zhang, Tingting , Tang, Zhenpeng . Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach . | FRONTIERS IN ENERGY RESEARCH , 2023 , 10 . |
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Understanding the risk spillover of the oil market in economic uncertainty is of great importance. However, it is difficult to take on a traditional single perspective in describing the risk spillover law of economic uncertainty in the crude oil market on different timescales. In order to fill the research gap resulting from such difficulty, this paper incorporates empirical mode decomposition into the time-varying Copula-CoVaR model, and for the first time explores the risk spillover path of economic uncertainty on the two international crude oil pricing benchmarks-Brent and West Texas Intermediate crude oil prices-using different timescales. The empirical results not only verify the necessity of research from the perspective of different timescales, but also reveal the heterogeneity of the risk spillover paths of different types of economic uncertainty on crude oil prices. The research in this paper provides a multi-perspective interpretation for understanding the complex risk spillovers between various economic uncertainties and the crude oil market, as well as providing meaningful information to support stakeholders in making rational decisions.
Keyword ：
Crude oil market Crude oil market Economic uncertainty Economic uncertainty Empirical mode decomposition Empirical mode decomposition Time-varying Copula-CoVaR model Time-varying Copula-CoVaR model
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| GB/T 7714 | Zhang, Tingting , Tang, Zhenpeng . The dependence and risk spillover between economic uncertainties and the crude oil market: new evidence from a Copula-CoVaR approach incorporating the decomposition technique [J]. | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2023 , 30 (47) : 104116-104134 . |
| MLA | Zhang, Tingting et al. "The dependence and risk spillover between economic uncertainties and the crude oil market: new evidence from a Copula-CoVaR approach incorporating the decomposition technique" . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30 . 47 (2023) : 104116-104134 . |
| APA | Zhang, Tingting , Tang, Zhenpeng . The dependence and risk spillover between economic uncertainties and the crude oil market: new evidence from a Copula-CoVaR approach incorporating the decomposition technique . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2023 , 30 (47) , 104116-104134 . |
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本文融合了二次分解与极限学习机的优势，提出了VMD-Res.-EEMD-ELM贵金属期货价格预测模型，选择变分模态分解(VMD)作为主要的分解技术，生成模态分量序列(VMF_i)和残差序列(Res.),采用集合经验模态分解(EEMD)对残差序列进行二次分解，并使用具有良好泛化能力的极限学习机(ELM)对各分量进行预测，最后叠加各模态分量和残差的预测值形成收益率的最终预测结果。所提出的模型不仅充分发挥了二次分解技术的优势，而且解决了传统变分模态分解组合预测模型未考虑残差影响因素的问题。实证研究表明，本文所提出的组合模型能够全面捕捉黄金、白银期货价格日收益率序列的特征，方向性预测准确率分别为83.33%和93.33%,误差指标MAE分别为0.15和0.11,经比较本文所提出的模型具有良好的预测性能。
Keyword ：
分解集成 分解集成 多模态集成预测 多模态集成预测 时间序列 时间序列 机器学习 机器学习 混合模型 混合模型
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| GB/T 7714 | 陈凯杰 , 唐振鹏 , 吴俊传 et al. 贵金属期货价格预测方法及实证研究 [J]. | 中国管理科学 , 2022 , 30 (12) : 245-253 . |
| MLA | 陈凯杰 et al. "贵金属期货价格预测方法及实证研究" . | 中国管理科学 30 . 12 (2022) : 245-253 . |
| APA | 陈凯杰 , 唐振鹏 , 吴俊传 , 张婷婷 , 杜晓旭 . 贵金属期货价格预测方法及实证研究 . | 中国管理科学 , 2022 , 30 (12) , 245-253 . |
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