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Carbon financial futures have both the characteristics of commodity futures and environmental protection and its price is affected by many factors. It is hard and complex for traditional analysis methods to get precise prediction results effectively. How to effectively predict the price trend of carbon financial futures has been focused on by both...
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Citations
... For example, Tang et al. (Tang et al., 2020) mention that the integration of artificial intelligence (AI) models and statistical models can improve the accuracy of the interpretability of carbon market research. Chai et al. and Chen et al.(Chai et al., 2020;Chen et al., 2021) use support vector machine (SVM) and particle swarm optimization (PSO) techniques, respectively, to forecast carbon prices, helping investors and risk managers in their decision-making. Therefore, future research should balance adequate models for carbon market-related research. ...
Green finance is an emerging topic which is broadly discussed in context of adapting and mitigating environmental deterioration due to climate change. As an effective incentive mechanism, it provides strong support for carbon emission reduction. However, a limited review articles investigate the specific combination of green finance and carbon emission reduction. Here, we apply a bibliometric analysis to review research on green finance and carbon emission reduction based on the literature from 2010 to 2021 in the Web of Science core database. The results indicate that countries with the most publications were those with high economic development, salient environmental problems, and a strong demand for ecological protection. Top publishing journals include Climate Policy, Journal of Cleaner Production, and Energy Policy. The author collaboration is fragmented, mostly less than three researchers. Based on analyses of keyword frequency and centrality, deforestation, carbon markets, and financial development were the most significant research topics. The research hotspots included clean development mechanism, adaptation, carbon market, and sequestration. Finally, the DPSIR framework is applied to explore driving forces, state, pressure, impact and response of current research. We hope our work provides a systematic review of green finance for carbon emission reduction to boost the research in this field.
... The graphical illustration and mathematical definition of OHLC data can refer to in Section 4.1. Although there has been a substantial body of literature devoted broadly to forecasting the price of EUA futures (e.g., Tsai and Kuo, 2014;Sun et al., 2016;Zhu et al., 2018;Liu and Shen, 2020), most of these studies only focused on the forecast of the close price of EUA futures (e.g., Fan et al., 2015;Atsalakis, 2016;Zhu et al., 2017;Chen et al., 2021). In contrast, there is a shortage of studies concerned with the structural forecasting of EUA futures price by treating the open-high-low-close prices of EUA futures as an integral data structure and jointly considering all four-dimensional prices. ...
... The results showed that the forecast accuracy of the EWT-GRU model was better than ARIMA, BPNN, GRU, and EWT-BPNN. Chen et al. (2021) addressed the high forecast error of EUA futures price by constructing a novel approach by combining SVM and particle swarm optimization (PSO) algorithms. Results indicated that the PSO-SVM algorithm could effectively forecast extreme price fluctuations and overcome high forecast errors caused by parameter constraints. ...
This paper develops an open-high-low-close (OHLC) data forecasting framework to forecast EUA futures price based on EU ETS data and extended exogenous variables from 2013 to 2020. The challenge of forecasting such an OHLC structure lies in handling its three intrinsic constraints, i.e., the positive constraint, interval constraint, and boundary constraint. This paper proposes a novel unconstrained transformation method for OHLC data and combines it with various forecasting models. Out-of-sample modelings identify the extraordinary performance of the convolutional neural network (CNN) in terms of MAPE (1.371%), MAE (0.274), RMSE (0.370), and AR (0.621), better than that of multiple linear regression (MLR), vector auto-regression (VAR) and vector error correction model (VECM), support vector regression (SVR), and multi-layer perceptron (MLP). The proposed transformation-based forecasting framework demonstrates the considerable potential for OHLC data forecasting in the energy finance field, e.g., crude and natural gas. Practicable and concrete suggestions are provided to ensure the profitability of trading EUA futures.
... By contrast with econometric models, machine learning models are effective in mining and retaining the valuable information in the data and in dealing with nonlinear data effectively [7]. Artificial neural network (ANN) [8,9], support vector machine (SVM) [10,11], long short-term memory (LSTM) [12], and ensemble learning models are some of the common models. Ensemble learning models can effectively combine the results of multiple base learners to achieve secondary learning of the problem with high generalizability. ...
An integrated hog futures price forecasting model based on whale optimization algorithm (WOA), LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices.
Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models’ universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model’s validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants’ behavior, which is more practical in the operation of carbon trading markets.
Graphical abstract
Faults are most likely to occur during the coding phase of software development. If, before the testing process, we can predict parts of code that are more prone to faults, then a large amount of time, software cost could be saved, and the software’s overall quality could be improved. Various researchers have previously attempted to predict software faults using numerous machine learning techniques in order to identify whether software modules are fault-prone or not. Ranking the software modules based on their fault content has rarely been explored before. Additionally, Bayesian methods have not been explored before for this task. We aim to investigate both linear and non-linear Bayesian regression methods for software fault prediction in this work. We develop and evaluate fault prediction models for two scenarios: intra-release prediction and cross-release prediction. The experimental investigation is conducted on 46 different software project versions. We use mean absolute error, and root means square error, and fault percentage average as performance measures. The results showed that Bayesian NLR outperformed linear regression and other used machine learning approaches or produced at least comparable performance. Bayesian linear regression method performed moderately.
Digital currency price prediction is vital to both sellers and purchasers. Over these years, decomposition and integration models have been applied more and more to realize the goal of precise prediction, however, many of them tend to neglect the reconstruction of features or the residual series. Altogether, one of the biggest drawbacks of the decomposition and integration framework is the method applied requires manual parameter setting whether it is for decomposition or integration. Still, for the results, they are merely satisfied with the point prediction which brings high uncertainty. In this paper, an optimized feature reconstruction decomposition and two-step nonlinear integration method is proposed which gives consideration to feature reconstruction, nonlinear integration, optimization and interval prediction. The original data series is decomposed through improved variational mode decomposition based approximate entropy feature reconstruction system. Then, improved particle swarm optimization-gated recurrent unit (iPSO-GRU) is utilized in the first and second nonlinear integration part separately. Meanwhile, the residual series is given attention, if it is not a white noise series, the residual will be the input of iPSO-GRU whose result will be added back to the second integration result to form the point prediction result. Based on the point prediction result, interval prediction estimate will be generated as well via maximum likelihood function. This study chooses three kinds of digital currency as cases and the results show that the MAPE values of point prediction are all below 3.5%, and CP values of interval prediction are all 1 with suitable MWP. In addition, compared with other benchmark models, the proposed model shows better performance.
