The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services p...The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.展开更多
As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation tec...As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation technologies is also an important means of reducing CO_(2)emissions and achieving the carbon peak and carbon neutral commitments.This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance(CA)trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated.We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional It?’s formula.In this manner,we obtained the models for optimal emission reduction and equilibrium CA price.Our numerical analysis revealed that,within a compliance period of 2021–2030,the optimal reductions and desired equilibrium prices of CAs changed concurrently,with an increasing trend annually in different peak-year scenarios.Furthermore,sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent,the depreciation rate positively impacted the CA price,and increasing the initial CA reduced the optimal reduction and the CA price.Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.展开更多
This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in Chi...This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.展开更多
The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns o...The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.展开更多
Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the n...Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity.展开更多
A quantitative research on the effect of coal mining on the soil organic carbon(SOC)pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-c...A quantitative research on the effect of coal mining on the soil organic carbon(SOC)pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining.Moreover,the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved.Tak-ing the Changhe River Basin of Jincheng City,Shanxi Province,China,as the study area,this paper proposed a radial basis function neural network model combined with the ordinary kriging method.The model includes topography and vegetation factors,which have large influence on soil properties in mining areas,as input parameters to predict the spatial distribution of SOC in the 0-20 and 2040 cm soil layers of the study area.And comparing the prediction effect with the direct kriging method,the results show that the mean error,the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method.Based on the fitting effect of the predicted and measured values,the R^(2) obtained by the radial basis artificial neural network are 0.81,0.70,respectively,higher than the value of 0.44 and 0.36 obtained by the direct kriging method.Therefore,the model combining the artificial neural network and kriging,and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.展开更多
Cars are regarded as an indispensable means of transportation in Taiwan.Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expand...Cars are regarded as an indispensable means of transportation in Taiwan.Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded.In this study,a price prediction system for used BMW cars was developed.Nine parameters of used cars,including their model,registration year,and transmission style,were analyzed.The data obtained were then divided into three subsets.The first subset was used to compare the results of each algorithm.The predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural network.The second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low,medium,and high parameters of the membership function(MF)to achieve model optimization.Finally,the third subset was used for the validation set during the prediction process.These three subsets were divided using k-fold cross-validation to avoid overfitting and selection bias.In conclusion,in this study,a model combining two optimal algorithms(i.e.,random forest and k-nearest neighbors)with several optimization algorithms(i.e.,gray wolf optimizer,multilayer perceptron,and MF)was successfully established.The prediction results obtained indicated a mean square error of 0.0978,a root-mean-square error of 0.3128,a mean absolute error of 0.1903,and a coefficient of determination of 0.9249.展开更多
The impact energy prediction model of low carbon steel was investigated based on industrial data. A three-layer neural network, extreme learning machine, and deep neural network were compared with different activation...The impact energy prediction model of low carbon steel was investigated based on industrial data. A three-layer neural network, extreme learning machine, and deep neural network were compared with different activation functions, structure parameters, and training functions. Bayesian optimization was used to determine the optimal hyper-parameters of the deep neural network. The model with the best performance was applied to investigate the importance of process parameter variables on the impact energy of low carbon steel. The results show that the deep neural network obtains better prediction results than those of a shallow neural network because of the multiple hidden layers improving the learning ability of the model. Among the models, the Bayesian optimization deep neural network achieves the highest correlation coefficient of 0.9536, the lowest mean absolute relative error of 0.0843, and the lowest root mean square error of 17.34 J for predicting the impact energy of low carbon steel. Among the variables, the main factors affecting the impact energy of low carbon steel with a final thickness of7.5 mm are the thickness of the original slab, the thickness of intermediate slab, and the rough rolling exit temperature from the specific hot rolling production line.展开更多
The carbonate rocks in Tahe oilfield, which suffered from multi-period polycycle karstification and structure deformation, are heterogeneous reservoirs that are rich in pores, cavities,and fractures. The reservoirs ar...The carbonate rocks in Tahe oilfield, which suffered from multi-period polycycle karstification and structure deformation, are heterogeneous reservoirs that are rich in pores, cavities,and fractures. The reservoirs are diversified in scale, space configuration, and complex in filling. For this kind of reservoir, a suite of seismic prestack or poststack prediction techniques has been developed based on the separation of seismic wave fields. Through cross-verification of the estimated results,a detailed description of palaeogeomorphology and structural features such as pores, cavities, and fractures in unaka has been achieved, the understanding of the spatial distribution of reservoir improved.展开更多
The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based...The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.展开更多
Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine lear...Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.展开更多
Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 s...Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 soil sampling points in the nearsurface layer(0 to 20 cm)in a representative region of northern China's agro-pastoral ecotone,we studied effects of four interpolation methods such as ordinary kriging(OK),universal kriging(UK),inverse distance weighting(IDW)and radial basis function(RBF)and random subsampling(50,100,200,300,400,and 500)on the prediction accuracy of SOC estimation.When the Shannon's Diversity Index(SHDI)and Shannon's Evenness Index(SHEI)was 2.01 and 0.67,the OK method appeared to be a superior method,which had the smallest root mean square error(RMSE)and the mean error(ME)nearest to zero.On the contrary,the UK method performed poorly for the interpolation of SOC in the present study.The sample size of 200 had the most accurate prediction;50 sampling points produced the worst prediction accuracy.Thus,we used 200 samples to estimate the study area's soil organic carbon density(SOCD)by the OK method.The total SOC storage to a depth of 20 cm in the study area was 117.94 Mt,and its mean SOCD was 2.40 kg/m2.The SOCD kg/(C⋅m2)of different land use types were in the following order:woodland(3.29)>grassland(2.35)>cropland(2.19)>sandy land(1.55).展开更多
The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the o...The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.展开更多
The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding t...The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.展开更多
A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential meth...A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.展开更多
Soil organic carbon (SOC) is an important indicator of soil degradation process. In this study, the long-term SOC evolution in Chinese mollisol farmland was simulated and predicted by validating, analyzing, processing...Soil organic carbon (SOC) is an important indicator of soil degradation process. In this study, the long-term SOC evolution in Chinese mollisol farmland was simulated and predicted by validating, analyzing, processing and assorting concerning data, based on clarifying parameters of Century model need, combined with best use of recorded data of field management, observed data of long-term experiments, climate, soil, and biology, and achieved results from Hailun Agro-Ecological Experimental Station, Chinese Academy of Sciences. The results were showed as follows: Before reclamation, SOC content was around 58.00 g kg-1. SOC content dropped quickly in early years, and then decreased slowly after reclamation. SOC content was around 34.00 g kg-1 with a yearly average rate of 8.91‰ decrease before long-term experiments was established. After a long-term experiment, SOC would change under different farming systems. Shift farming system changed as follows: By 20-year model simulation, SOC content decreased from 34.03 to 30.19 g kg-1, with a yearly average rate of 5.97‰; by 100-year model simulation, SOC content decreased to 24.31 g kg-1, with a yearly average rate of 3.36‰. Organic farming system changed as follows: By 20-year model simulation, SOC content decreased slowly from 34.03 to 33.39 g kg-1, with a yearly average rate of 0.95‰, 5‰ less than that of shift farming system; by 100-year model simulation, SOC content decreased to 32.21 g kg-1, with a yearly average rate of 0.55‰. "Petroleum" farming system changed as follows: By 20-year model simulation, SOC content decreased from 34.03 to 32.88 g kg-1, with a yearly average rate of 1.72‰, much more than that of organic farming system; by 100-year model simulation, SOC content decreased to 30.89 g kg-1, with a yearly average rate of 0.96‰. Combined "petroleum"-organic farming system changed as follows: By 20-year model simulation, SOC content was increased slightly; by 100-year model simulation, SOC content increased from 34.03 to 34.41g kg-1, with a yearly average rate of 0.11‰. The above results provided an optimal way for maintaining SOC in Chinese mollisol farmland: To increase, as much as possible within agro-ecosystem, soil organic matter returns such as crop stubble, crop litter, crop straw or stalk, and manure, besides applying chemical nitrogen and phosphorous, which increased system productivity and maintained SOC content as well. Also, the results provided a valuable methodology both for a study of CO2 sequestration capacity and for a target fertility determination in Chinese mollisol.展开更多
This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China.In the process of...This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China.In the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and error.Finally,this model is applied to predict the retail prices of eggs and compared with ARIMA.The result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of eggs.The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.展开更多
We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models...We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.展开更多
Under the pressure of sustained growth in energy consumption in China,the implementation of a carbon pricing mechanism is an effective economic policy measure for promoting emission reduction,as well as a hotspot of r...Under the pressure of sustained growth in energy consumption in China,the implementation of a carbon pricing mechanism is an effective economic policy measure for promoting emission reduction,as well as a hotspot of research among scholars and policy makers.In this paper,the effects of carbon prices on Beijing's economy are analyzed using input-output tables.The carbon price costs are levied in accordance with the products'embodied carbon emission.By calculation,given the carbon price rate of 10 RMB/t-CO_2,the total carbon costs of Beijing account for approximately 0.22-0.40%of its gross revenue the same year.Among all industries,construction bears the largest carbon cost Among export sectors,the coal mining and washing industry has much higher export carbon price intensity than other industries.Apart from traditional energy-intensive industries,tertiary industry,which accounts for more than 70%of Beijing's economy,also bears a major carbon cost because of its large economic size.However,from 2007 to 2010,adjustment of the investment structure has reduced the emission intensity in investment sectors,contributing to the reduction of overall emissions and carbon price intensity.展开更多
A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and ...A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and the sort of matrix are the key controlling factors for its ablative performance.Then,a brief fuzzy mathe- matical relationship was established between these factors and ablative performance.Through experiments, the performance of the ANN was evaluated,which was used in the ablative performance prediction of C/C composites.When the training set,the structure and the training parameter of the net change,the best match ratio of these parameters was achieved.Based on the match ratio,this paper forecasts and evalu- ates the carbon-carbon ablation performance.Through experiences,the ablative performance prediction of carbon-carbon using ANN can achieve the line ablation rate,which satisfies the need of precision of practical engineering fields.展开更多
文摘The consumer price index (CPI) measures the relative number of changes in the price level of consumer goods and services over time, reflecting the trend and degree of changes in the price level of goods and services purchased by residents. This article uses the ARMA model to analyze the fluctuation trend of the CPI (taking Chongqing as an example) and make short-term predictions. To test the predictive performance of the model, the observation values from January to December 2023 were retained as the reference object for evaluating the predictive accuracy of the model. Finally, through trial predictions of the data from May to August 2023, it was found that the constructed model had good fitting performance.
基金like to thank Major Program of National Philosophy and Social Science Foundation of China(Grant No.21ZDA086)National Natural Science Foundation of China(Grant No.71974188),and Jiangsu Soft Science Fund(Grant No.BR2022007).
文摘As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation technologies is also an important means of reducing CO_(2)emissions and achieving the carbon peak and carbon neutral commitments.This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance(CA)trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated.We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional It?’s formula.In this manner,we obtained the models for optimal emission reduction and equilibrium CA price.Our numerical analysis revealed that,within a compliance period of 2021–2030,the optimal reductions and desired equilibrium prices of CAs changed concurrently,with an increasing trend annually in different peak-year scenarios.Furthermore,sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent,the depreciation rate positively impacted the CA price,and increasing the initial CA reduced the optimal reduction and the CA price.Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.
基金supports from the National Natural Science Foundation of China(under Grants No.72073105,71903002,and 71774122)the Natural Science Foundation of Anhui Province,China(under Grant No.1908085QG309)are greatly acknowledged.
文摘This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.
文摘The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.
文摘Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity.
基金supported by the National Natural Science Foundation of China (51304130)the Natural Science Foundation of Shanxi Province,China (2015021125)+4 种基金Shanxi Provincial People's Government Major Decision Consulting Project (ZB20211703)Program for the Soft Science research of Shanxi (2018041060-2)Program for the Philosophy and Social Sciences Research of Higher Learning Institutions of Shanxi (201803010)Philosophy and Social Sciences Planning Project of Shanxi Province (2020YJ052)Basic Research Program of Shanxi Province (20210302123403).
文摘A quantitative research on the effect of coal mining on the soil organic carbon(SOC)pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining.Moreover,the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved.Tak-ing the Changhe River Basin of Jincheng City,Shanxi Province,China,as the study area,this paper proposed a radial basis function neural network model combined with the ordinary kriging method.The model includes topography and vegetation factors,which have large influence on soil properties in mining areas,as input parameters to predict the spatial distribution of SOC in the 0-20 and 2040 cm soil layers of the study area.And comparing the prediction effect with the direct kriging method,the results show that the mean error,the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method.Based on the fitting effect of the predicted and measured values,the R^(2) obtained by the radial basis artificial neural network are 0.81,0.70,respectively,higher than the value of 0.44 and 0.36 obtained by the direct kriging method.Therefore,the model combining the artificial neural network and kriging,and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.
基金This work was supported by the Ministry of Science and Technology,Taiwan,under Grants MOST 111-2218-E-194-007.
文摘Cars are regarded as an indispensable means of transportation in Taiwan.Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded.In this study,a price prediction system for used BMW cars was developed.Nine parameters of used cars,including their model,registration year,and transmission style,were analyzed.The data obtained were then divided into three subsets.The first subset was used to compare the results of each algorithm.The predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural network.The second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low,medium,and high parameters of the membership function(MF)to achieve model optimization.Finally,the third subset was used for the validation set during the prediction process.These three subsets were divided using k-fold cross-validation to avoid overfitting and selection bias.In conclusion,in this study,a model combining two optimal algorithms(i.e.,random forest and k-nearest neighbors)with several optimization algorithms(i.e.,gray wolf optimizer,multilayer perceptron,and MF)was successfully established.The prediction results obtained indicated a mean square error of 0.0978,a root-mean-square error of 0.3128,a mean absolute error of 0.1903,and a coefficient of determination of 0.9249.
基金financially supported by the National Natural Science Foundation of China (No.U1960202)the China Post-doctoral Science Foundation funded Projects (No.2019M651467)the Natural Science Foundation Joint Fund Project of Liaoning Province, China (No.2019-KF-2506)。
文摘The impact energy prediction model of low carbon steel was investigated based on industrial data. A three-layer neural network, extreme learning machine, and deep neural network were compared with different activation functions, structure parameters, and training functions. Bayesian optimization was used to determine the optimal hyper-parameters of the deep neural network. The model with the best performance was applied to investigate the importance of process parameter variables on the impact energy of low carbon steel. The results show that the deep neural network obtains better prediction results than those of a shallow neural network because of the multiple hidden layers improving the learning ability of the model. Among the models, the Bayesian optimization deep neural network achieves the highest correlation coefficient of 0.9536, the lowest mean absolute relative error of 0.0843, and the lowest root mean square error of 17.34 J for predicting the impact energy of low carbon steel. Among the variables, the main factors affecting the impact energy of low carbon steel with a final thickness of7.5 mm are the thickness of the original slab, the thickness of intermediate slab, and the rough rolling exit temperature from the specific hot rolling production line.
文摘The carbonate rocks in Tahe oilfield, which suffered from multi-period polycycle karstification and structure deformation, are heterogeneous reservoirs that are rich in pores, cavities,and fractures. The reservoirs are diversified in scale, space configuration, and complex in filling. For this kind of reservoir, a suite of seismic prestack or poststack prediction techniques has been developed based on the separation of seismic wave fields. Through cross-verification of the estimated results,a detailed description of palaeogeomorphology and structural features such as pores, cavities, and fractures in unaka has been achieved, the understanding of the spatial distribution of reservoir improved.
基金the Key Project of National Natural Science Foundation of China(No.61134009)Program for Changjiang Scholars and Innovation Research Team in University from the Ministry of Education,China(No.IRT1220)+1 种基金Specialized Research Fund for Shanghai Leading Talents,Project of the Shanghai Committee of Science and Technology,China(No.13JC1407500)the Fundamental Research Funds for the Central Universities,China(No.2232012A3-04)
文摘The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.
文摘Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.
基金This research was supported by the National Key R and D Program of China(2016YFC0500901 and 2016YFC0500907)the National Natural Science Foundation of China(Grant Nos.31971466 and 41807525)the One Hundred Person Project of the Chinese Academy of Sciences(Y551821).
文摘Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 soil sampling points in the nearsurface layer(0 to 20 cm)in a representative region of northern China's agro-pastoral ecotone,we studied effects of four interpolation methods such as ordinary kriging(OK),universal kriging(UK),inverse distance weighting(IDW)and radial basis function(RBF)and random subsampling(50,100,200,300,400,and 500)on the prediction accuracy of SOC estimation.When the Shannon's Diversity Index(SHDI)and Shannon's Evenness Index(SHEI)was 2.01 and 0.67,the OK method appeared to be a superior method,which had the smallest root mean square error(RMSE)and the mean error(ME)nearest to zero.On the contrary,the UK method performed poorly for the interpolation of SOC in the present study.The sample size of 200 had the most accurate prediction;50 sampling points produced the worst prediction accuracy.Thus,we used 200 samples to estimate the study area's soil organic carbon density(SOCD)by the OK method.The total SOC storage to a depth of 20 cm in the study area was 117.94 Mt,and its mean SOCD was 2.40 kg/m2.The SOCD kg/(C⋅m2)of different land use types were in the following order:woodland(3.29)>grassland(2.35)>cropland(2.19)>sandy land(1.55).
基金supported by National Basic Research Program(2006CB202304)of Chinaco-supported by the National Basic Research Program of China(Grant No.2011CB201103)the National Science and Technology Major Project of China(Grant No.2011ZX05004003)
文摘The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.
文摘The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.
基金the financial support from the National Natural Science Foundation of China (No. 51274230)the Natural Science Foundation of Shandong Province (No. ZR2012EEL01)the Fundamental Research Funds for the Central Universities (No. 14CX02040A and No. 14CX06023A)
文摘A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.
基金grants from Dis-tinguished Young Scholar Fund of Heilongjiang Prov-ince (JC200718)the National 863 Program of China(2006AA10Z424)
文摘Soil organic carbon (SOC) is an important indicator of soil degradation process. In this study, the long-term SOC evolution in Chinese mollisol farmland was simulated and predicted by validating, analyzing, processing and assorting concerning data, based on clarifying parameters of Century model need, combined with best use of recorded data of field management, observed data of long-term experiments, climate, soil, and biology, and achieved results from Hailun Agro-Ecological Experimental Station, Chinese Academy of Sciences. The results were showed as follows: Before reclamation, SOC content was around 58.00 g kg-1. SOC content dropped quickly in early years, and then decreased slowly after reclamation. SOC content was around 34.00 g kg-1 with a yearly average rate of 8.91‰ decrease before long-term experiments was established. After a long-term experiment, SOC would change under different farming systems. Shift farming system changed as follows: By 20-year model simulation, SOC content decreased from 34.03 to 30.19 g kg-1, with a yearly average rate of 5.97‰; by 100-year model simulation, SOC content decreased to 24.31 g kg-1, with a yearly average rate of 3.36‰. Organic farming system changed as follows: By 20-year model simulation, SOC content decreased slowly from 34.03 to 33.39 g kg-1, with a yearly average rate of 0.95‰, 5‰ less than that of shift farming system; by 100-year model simulation, SOC content decreased to 32.21 g kg-1, with a yearly average rate of 0.55‰. "Petroleum" farming system changed as follows: By 20-year model simulation, SOC content decreased from 34.03 to 32.88 g kg-1, with a yearly average rate of 1.72‰, much more than that of organic farming system; by 100-year model simulation, SOC content decreased to 30.89 g kg-1, with a yearly average rate of 0.96‰. Combined "petroleum"-organic farming system changed as follows: By 20-year model simulation, SOC content was increased slightly; by 100-year model simulation, SOC content increased from 34.03 to 34.41g kg-1, with a yearly average rate of 0.11‰. The above results provided an optimal way for maintaining SOC in Chinese mollisol farmland: To increase, as much as possible within agro-ecosystem, soil organic matter returns such as crop stubble, crop litter, crop straw or stalk, and manure, besides applying chemical nitrogen and phosphorous, which increased system productivity and maintained SOC content as well. Also, the results provided a valuable methodology both for a study of CO2 sequestration capacity and for a target fertility determination in Chinese mollisol.
基金financially supported by the National KeyTechnology R&D Program during the 12th Five-Year Plan period(2012BAH20B04)the 948 Program of Ministry of Agriculture,China(2013-Z1)
文摘This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China.In the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and error.Finally,this model is applied to predict the retail prices of eggs and compared with ARIMA.The result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of eggs.The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.
文摘We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.
基金The authors would like to thank Key Projects in the National Science&Technology Pillar Program during the Twelfth Five Year Plan Period[grant number 2012BAC20B03]Beijing Natural Science Foundation[grant number 9112008]for supporting this research
文摘Under the pressure of sustained growth in energy consumption in China,the implementation of a carbon pricing mechanism is an effective economic policy measure for promoting emission reduction,as well as a hotspot of research among scholars and policy makers.In this paper,the effects of carbon prices on Beijing's economy are analyzed using input-output tables.The carbon price costs are levied in accordance with the products'embodied carbon emission.By calculation,given the carbon price rate of 10 RMB/t-CO_2,the total carbon costs of Beijing account for approximately 0.22-0.40%of its gross revenue the same year.Among all industries,construction bears the largest carbon cost Among export sectors,the coal mining and washing industry has much higher export carbon price intensity than other industries.Apart from traditional energy-intensive industries,tertiary industry,which accounts for more than 70%of Beijing's economy,also bears a major carbon cost because of its large economic size.However,from 2007 to 2010,adjustment of the investment structure has reduced the emission intensity in investment sectors,contributing to the reduction of overall emissions and carbon price intensity.
基金supported by the National Natural Science Foundation of China under Grant No.10572044.
文摘A preliminary estimation of ablation property for carbon-carbon composites by artificial neutral net (ANN) method was presented.It was found that the carbon-carbon composites' density,degree of graphitization and the sort of matrix are the key controlling factors for its ablative performance.Then,a brief fuzzy mathe- matical relationship was established between these factors and ablative performance.Through experiments, the performance of the ANN was evaluated,which was used in the ablative performance prediction of C/C composites.When the training set,the structure and the training parameter of the net change,the best match ratio of these parameters was achieved.Based on the match ratio,this paper forecasts and evalu- ates the carbon-carbon ablation performance.Through experiences,the ablative performance prediction of carbon-carbon using ANN can achieve the line ablation rate,which satisfies the need of precision of practical engineering fields.
