“中国梦”实现过程的中国经济总量和人民生活水平预测

文章分析了“中国梦”实现的三个阶段中,中国经济总量和人民生活水平将会发生的变化。文章预测：按实际汇率换算,2021年中国经济总量将超越美国成为世界第一经济大国,此时中国的人民生活水平将达到当时的世界平均水平;2049年中国的人民生活水平将达到世界高收入国家90%的生活水平;2065年中国的人民生活水平将达到最发达国家的生活水平,进而全面实现几代中国人梦寐以求的中华民族的伟大复兴。     

开采急倾斜煤层沉陷预测实例研究

沉陷预测是煤炭项目环境影响评价中最核心内容之一,其准确性直接关系到生态保护措施的有效性及可行性。急倾斜煤层由于采深变化大、顶底板条件不同,且没有实测的移动变形参数,采用常规概率积分法预测沉陷范围可能出现的偏差较大。以贵州省仁怀保利煤矿环评为实例,依据不同开采时段,分别采用深度积分及等价工作面方法预测沉陷范围。采用该方法对急倾斜煤层作沉陷预测更加符合实际,为其他类似矿井对环境影响评价的沉陷预测提供了参考。     

基于BG/NBD模型的电信客户终身价值测度研究

在技术水平不断提升以及产品服务趋于同质化的环境下,我国移动通信企业开始由基础网络建设向专业化的增值业务转型,在激烈的市场竞争中,及时准确的挖掘高价值客户,提供个性化的功能业务,提升客户价值,成为各通信企业获取新竞争优势的有效途径之一。在BG/NBD模型的基础上,通过捕捉客户随机交易行为,构建了客户终身价值测度模型,结合电信运营商的客户数据进行客户终身价值预测研究,结果发现,模型在客户整体层面和个体层面均能有效且准确的预测客户终身价值。     

Inconvenience cost of mobile communication failure: The case of South Korea

Mobile communication failure can occur when mobile traffic exceeds the manageable level. This depends on frequency bandwidth. Mobile communication failure causes inconveniences in a user's daily life that lead to social and economic damage. To address this issue, mobile telecommunications companies deploy additional bandwidths and develop new technologies, but these are costly strategies. This study applies a spike model based on a contingent valuation method (CVM) to measure the inconvenience cost resulting from mobile communication failure. The mean monthly willingness-to-pay (WTP) to avoid communication failure per user is estimated to be KRW 898.14 (USD 0.80) over a period of five years in our study. The inconvenience cost borne by the population is estimated to be KRW 2.97 trillion (USD 2.61 billion). Users experiencing greater frequency of communication failure are found to be willing to pay more to avoid the inconvenience. When excluding respondents citing zero-WTP, the mean WTP per user was calculated to be KRW 3426.41 (USD 3.01). Data traffic usage and frequency at which communication failure is experienced are variables that exhibit statistically significant effects on WTP to avoid mobile communication failure. Overall, estimation results show that a price discrimination based on data traffic usage or quality can be considered by mobile telecommunications companies and regulators to address the issue of data traffic inducing mobile communication failure.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

山西王家岭煤矿底板奥灰水突水危险性评价

我国是受水害最严重的国家,奥灰水突水是我国很多煤矿在安全生产中急待解决的问题。山西王家岭煤业集团公司王家岭煤矿就面临着开采9号煤层底板奥灰水突水威胁的安全问题。结合王家岭煤矿实际钻孔数据,运用突水系数法、多层叠加抗压强度比值系数法分别对9号煤层进行突水危险性预测,并进行相互对比,根据计算所得结果得出9号煤层底板易发生突水区。     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

对江苏省电力负荷进行回归预测

本文主要根据2001-2011年江苏省用电量样本数据,建立了江苏省电力负荷与人均GDP、工业化以及人口数之间的多元回归预测方程,并预测了江苏省2014-2020年总用电量数据,在此基础上提出了相应的建议。     

基于SVM的房地产财务决策风险预测及敏感性分析

本文以房地产上市公司的财务数据为参考依据,结合相关性分析,筛选并建立影响财务风险的指标体系;利用支持向量机建立财务风险分析预测模型;并使用灰色关联理论对指标进行敏感性分析,从而得到影响财务风险最敏感的指标。