企业财务危机预测的贝叶斯模型研究

本文探讨商业银行如何利用贝叶斯分类技术构建企业客户财务危机预测模型。本文使用财务比率作为评价企业绩效的特征属性,并考察两个不同的贝叶斯模型在估计企业客户发生财务危机的后验概率方面的有效性。一个比较简单但有较多的假设,即朴素贝叶斯模型;另一个某种程度上更为复杂但有更少的假设,即组合属性贝叶斯模型。研究发现,与朴素贝叶斯模型相比,由于组合属性贝叶斯模型更好地反映了变量之间潜在的联合分布,因此它能在历史数据支持下估计所要求的概率并做出更精确的预测。所提出的模型可以作为辅助银行审核者做出正确而快速决策的有用工具。     

报童模型及ARMA预测在航空配餐问题中的应用

航班承载人数的不确定性,造成航空公司在配餐中利润的流失,现存的配餐模式存在较多的浪费.本文利用基于损失厌恶的报童模型和ARMA时间序列分析模型对深圳航空公司某航班的配餐份数进行了建模分析和预测,并通过对两种模型输出的比较,得出了长期预测与短期预测的模型应用理论.将实际的历史数据代人到模型中验证,其结果优于经验模式下的配餐盈利情况.本文所采用的研究方法和研究结果对航空公司的精益发展有建设性的意义.     

网站用户登录信息挖掘特征规则

本文首先研究了基本的面向属性归纳，提出了从网站用户登录信息中挖掘用户特征规则的思路。问题从解决多属性归纳着手，特点是首先建立概念树，然后用条件属性组合与约束的方法进行挖掘。实验表明，本文设计的模型能客观地描述客户行为，为从Web信息挖掘客户特征规则提出了一条有效的途径。     

基于SMOTERF算法处理客户流失不平衡问题的研究

数据不平衡性是目前数据挖掘面临的主要问题之一.在客户流失预测研究中,数据不平衡的问题影响预测精度,导致准确率低,AUC值变小.传统重采样方法虽然能够解决数据不平衡问题,但会导致有效信息缺失、数据过度拟合等问题,为避免问题发生文中采用SMOTERF法,针对客户流失数据进行平衡后再用分类预测能较大地解决数据不平衡问题,且准确率高,AUC值大,效果较好.近年来服务业对客户流失的关切度越来越高,研究如何在原有的客户信息上去预测未来客户流失状况作出及时的应对措施,减少企业损失有重要意义.在客户流失预测中,对于解决数据的不平衡表现尤为重要.     

基于Group-LASSO方法的广义半参数可加信用评分模型应用研究

信用风险是目前商业银行面临的风险中最为重要和最为复杂的,新巴塞尔协议要求各国条件的银行通过实施内部评级法来度量并控制信用风险,内部评级法即通过银行收集的客户相历史数据来构建数学模型,测算客户的违约概率进而对客户进行评分。文章针对信用评分模型解释变量维数较高,类型丰富,好坏客户类型数量不均衡等特点,利用广义半参数可加模型对户违约概率进行建模,并将Group LASSO方法应用于模型进行变量选择和估计。实证研究表明本文提出的模型和方法与以往常用的线性logistic回归模型相比,在模型的判别能力和预测能以及解释性和计算效率上均有较大优势。     

基于数据挖掘技术的航空客户流失与细分研究及R语言程序实现

提高航空客运的上座率既能使航空资源得到充分利用,更能显著的增加航空公司的效益.主要从某航空公司旅客乘机记录中对航空公司的客户进行行为分析,采用数据挖掘技术,首先利用决策树方法对客户进行流失预测,然后利用K-均值聚类进行客户细分,将客户划分为五类,同时对客户进行价值评估,挖掘出有价值的客户,最后综合分析客户流失与客户细分结果,并提出相应的营销策略,从而达到提高上座率和效益的目标.     

一种基于季节指数的灰色马尔科夫气温预测模型

提出一种根据气温历史数据的年际周期性和季节性变化规律建立的基于季节指数的灰色-马尔科夫气温预测模型.模型将纵向与横向分析相结合方法运用到气温预报之中,通过季节指数修正气温的横向季节性变化,再用灰色模型进行预测,最后通过马尔科夫进行误差修正.实例运用中,对广州市的2000年月平均气温进行预测,在与历史数据的对比中表明,模型预测结果较为准确,可靠性较好.并讨论说明该模型也可推广到其他具有周期特征的非平稳时间序列的预测中,并大大提高预测精度.     

基于覆盖度的银行客户分类模型

投资收入是银行的主要收入来源之一。近年来,客户的投资意向显著降低,银行的投资收入减少。因此,银行希望将营销工作重点放在订阅概率更高的客户上。然而,繁多的客户样本以及客户信息导致银行的客户筛选工作低效。面对庞大的客户信息数据,粗糙集理论可以在不影响决策分类结果的前提下,通过属性约简删除属性集中的冗余属性,并导出问题的决策规则,提高效率。但在传统的属性约简过程中,没有针对进入正域的噪声样本进行控制,导致噪声特征被加入约简集。本文基于高效的相关族定义覆盖度,限制进入正域的样本。实验结果表明,覆盖度算法能够提高分类算法准确度及稳定性。最后,本文并将算法运用于银行客户分类实际问题中,剔除无用信息,筛选出关键属性,提高了分类准确度和稳定性,构建简洁高效的银行客户分类模型。     

烟台地区降水量的AR IMA随机模型研究

采用自回归求积移动平均法(AR IM A),对烟台地区历年来的降水量动态数据进行了分析.结果显示,AR IM A(3,1,2)模型提供了较准确的预测效果,相对误差变化在0.21%~5.75%,可以用于未来的预测,并为烟台市降水量的预测提供了可靠依据.     

"已实现"波动率在VaR计算中的实证研究

本文根据“已实现”波动率的性质用ARF IM A模型对其进行模拟,并在此基础上研究了V aR,发现在学生T分布和GED分布下有比较好的预测效果.     

Neighborhood based decision-theoretic rough set models

As an extension of Pawlak rough set model, decision-theoretic rough set model (DTRS) adopts the Bayesian decision theory to compute the required thresholds in probabilistic rough set models. It gives a new semantic interpretation of the positive, boundary and negative regions by using three-way decisions. DTRS has been widely discussed and applied in data mining and decision making. However, one limitation of DTRS is its lack of ability to deal with numerical data directly. In order to overcome this disadvantage and extend the theory of DTRS, this paper proposes a neighborhood based decision-theoretic rough set model (NDTRS) under the framework of DTRS. Basic concepts of NDTRS are introduced. A positive region related attribute reduct and a minimum cost attribute reduct in the proposed model are defined and analyzed. Experimental results show that our methods can get a short reduct. Furthermore, a new neighborhood classifier based on three-way decisions is constructed and compared with other classifiers. Comparison experiments show that the proposed classifier can get a high accuracy and a low misclassification cost.     

Bayesian modelling of financial guarantee insurance

In this paper we model the claim process of financial guarantee insurance, and predict the pure premium and the required amount of risk capital. The data used are from the financial guarantee system of the Finnish statutory pension scheme. The losses in financial guarantee insurance may be devastating during an economic depression (i.e., deep recession). This indicates that the economic business cycle, and in particular depressions, must be taken into account in modelling the claim amounts in financial guarantee insurance. A Markov regime-switching model is used to predict the frequency and severity of future depression periods. The claim amounts are predicted using a transfer function model where the predicted growth rate of the real GNP is an explanatory variable. The pure premium and initial risk reserve are evaluated on the basis of the predictive distribution of claim amounts. Bayesian methods are applied throughout the modelling process. For example, estimation is based on posterior simulation with the Gibbs sampler, and model adequacy is assessed by posterior predictive checking. Simulation results show that the required amount of risk capital is high, even though depressions are an infrequent phenomenon.     

Manufacturing lead time estimation using data mining

We explore use of data mining for lead time estimation in make-to-order manufacturing. The regression tree approach is chosen as the specific data mining method. Training and test data are generated from variations of a job shop simulation model. Starting with a large set of job and shop attributes, a reasonably small subset is selected based on their contribution to estimation performance. Data mining with the selected attributes is compared with linear regression and three other lead time estimation methods from the literature. Empirical results indicate that our data mining approach coupled with the attribute selection scheme outperforms these methods.     

导出匹配可扩图的局部运算

称图G为导出匹配图可扩的（简称为IM－可扩的），如果图G的一每个导出匹配都包含在G的一个完美匹配中，本给出了导出匹配可扩图的一些局部运算。     

时序多指标决策的灰色关联分析法

本文对带有时间顺序的混合型多指标决策，运用灰色关联理论，建立了一种新的灰色关联决策模型，从而为时序多指标决策问题提供了又一科学，合理的决策方法。     

An efficient rough feature selection algorithm with a multi-granulation view

Feature selection is a challenging problem in many areas such as pattern recognition, machine learning and data mining. Rough set theory, as a valid soft computing tool to analyze various types of data, has been widely applied to select helpful features (also called attribute reduction). In rough set theory, many feature selection algorithms have been developed in the literatures, however, they are very time-consuming when data sets are in a large scale. To overcome this limitation, we propose in this paper an efficient rough feature selection algorithm for large-scale data sets, which is stimulated from multi-granulation. A sub-table of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct. Because of that the total time spent on computing reducts for sub-tables is much less than that for the original large-scale one, the algorithm yields in a much less amount of time a feature subset (the approximate reduct). According to several decision performance measures, experimental results show that the proposed algorithm is feasible and efficient for large-scale data sets.     

时序多指标决策及其在证券投资中的应用

对于带有时间顺序的动态多指标决策问题,我们从专家偏好和信息熵两方面综合确定了指标权重,进而在关联分析的基础上,建立了时序多指标决策综合优化模型,并将其应用于证券投资领域.     

滋长阶小于1的亚纯函数唯一性

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SARS疫情对某些经济指标影响的评估模型

针对某市最近6年来的历史数据,建立了灰色预测模型,预测出2003年正常情况下部分经济指标的发展规律,再根据实际统计数据,从而可以计算出因SARS疫情对2003年相应的经济指标所造成的损失做出评估.     

Forecasting cancellation rates for services booking revenue management using data mining

Revenue management (RM) enhances the revenues of a company by means of demand-management decisions. An RM system must take into account the possibility that a booking may be canceled, or that a booked customer may fail to show up at the time of service (no-show). We review the Passenger Name Record data mining based cancellation rate forecasting models proposed in the literature, which mainly address the no-show case. Using a real-world dataset, we illustrate how the set of relevant variables to describe cancellation behavior is very different in different stages of the booking horizon, which not only confirms the dynamic aspect of this problem, but will also help revenue managers better understand the drivers of cancellation. Finally, we examine the performance of the state-of-the-art data mining methods when applied to Passenger Name Record based cancellation rate forecasting.