Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999

Impacts of complex emergencies or relief interventions have often been evaluated by absolute mortality compared to international standardized mortality rates. A better evaluation would be to compare with local baseline mortality of the affected populations. A projection of population-based survival data into time of emergency or intervention based on information from before the emergency may create a local baseline reference. We find a log-transformed Gaussian time series model where standard errors of the estimated rates are included in the variance to have the best forecasting capacity. However, if time-at-risk during the forecasted period is known then forecasting might be done using a Poisson time series model with overdispersion. Whatever, the standard error of the estimated rates must be included in the variance of the model either in an additive form in a Gaussian model or in a multiplicative form by overdispersion in a Poisson model. Data on which the forecasting is based must be modelled carefully concerning not only calendar-time trends but also periods with excessive frequency of events (epidemics) and seasonal variations to eliminate residual autocorrelation and to make a proper reference for comparison, reflecting changes over time during the emergency. Hence, when modelled properly it is possible to predict a reference to an emergency-affected population based on local conditions. We predicted childhood mortality during the war in Guinea-Bissau 1998-1999. We found an increased mortality in the first half-year of the war and a mortality corresponding to the expected one in the last half-year of the war.     

信息条件下的城市运输规划方法

交通网络中信息的引入,改变了以往人们的出行行为。常规的出行需求预测模型在信息条件下,需要进行合理改进。本文提出了将传统的出行产生模型与动态交通模拟模型集成进行信息条件下的城市运输规划研究的一种新的方法框架     

制造企业需求管理研究

文章介绍进入买方市场后 ,产能有限的制造企业进行需求管理的重要性 ,对需求管理的内涵、基本内容 (包括销售预测、订单管理、出货管理、销售分析 )、方法工具和工作流程进行具体分析。     

组合预测误差信息矩阵研究

研究组合预测误差信息矩阵的结构与组合预测方法性质之间的联系,首次提出冗余信息概念,对最优组合预测方法的组合结构进行了研究。     

经济预测中的Monte Carlo模拟及方差控制——以国际石油价格预测为例

从MDnte Carlo模拟的实现过程入手，首先通过对Monte Carlo方法原理的阐述来介绍该种方法。进一步结合具体的实例通过计算机进行模拟来解释Monte Carlo方法的具体实现过程。重点讨论在选择合理的数据生成过程的前提下，如何在Monte Carlo方法中减少模拟方差，从而提高估计精度，更好地应用这种方法来进行经济预测。     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

基于向量夹角余弦的组合预测模型的性质研究

基于向量夹角余弦的组合预测是一种相关性的组合预测模型,它是研究组合预测方法的一个新途经.针对基于向量夹角余弦准则下组合预测模型,研究它的基本结构特征.首先提出新的优性组合预测、预测方法优超、冗余度等概念.然后探讨了非劣性组合预测、优性组合预测以及冗余预测方法的存在性,并给出冗余信息的判定定理.最后进行实例分析,表明该方法有较大的实际应用价值.     

基于定性模拟的员工离职行为预测

员工是企业最具生命力的资源,员工的主动离职往往会给企业造成不必要的损失.综合国内外有关员工离职问题的研究,构建了一个员工离职过程模型,将定性模拟技术引入其中,根据QSIM算法的思路,结合有关员工离职行为的常识、专家知识设计了模拟规则,用VB编程并进行了模拟,模拟结果比较符合实际,表明这一方法可以用于员工离职预测的虚拟实验研究.     

供应链环境下合作预测效果的分析

本文定量分析了供应链中合作预测对需求长鞭效应的减小作用,在多节点企业所组成的供应链中,合作预测对订单量的波动和需求预测误差的波动起到积极的抑制作用。本文通过理论研究和数值分析的结果表明,供应链信息共享和合作预测能够控制需求流动现象。     

党政机构微博在突发事件中传播效果的影响因素及策略

在分析铁路货运量预测方法的基础上，针对标准BP神经网络的不足，提出改进的BP神经网络预测模型。首先,利用动态陡度因子来改变激励函数的陡峭程度，以此来得到更好的激励函数响应特征以及更好的非线性表达能力；其次，利用附加动量因子，通过对以前经验的积累，既降低了神经网络对误差曲面的局部细节敏感特性，又较好的遏制了神经网络易于限于局部最小的缺陷；最后，采取改变学习率的方法，给定一个较大的学习率初始值，在学习的过程中学习率不断减小，网络最终趋于稳定。改进BP算法既可以得到更优的解，还能够缩短训练时间。利用全国铁路货运