最优组合预测方法及其应用

组合预测方法是当前预测科学研究得最热烈的课题之一．组合预测的基本含义是把两个或两个以上的预测模型采用加权平均的方式组合为一个模型．组合预测的关键是确定各个组合系数或加权系数．关于加权系数的确定还有待于深入研究．有的认为加权系数可以事先人为设定［１］，有的认为可以采用层次分析法或二项式系数法来确定［２］，有的采用简单平均法来确定［３］．本文以组合预测误差平方和为目标函数．通过使误差平方和极小化来确定最优加权系数．本文给出了计算任意有限ｎ个预测模型组合的加权系数的公式，并导出了算术平均预测是最优预测的条件．最后还给出了应用实例．     

岭估计在上海居民生活用电预测中的应用

负荷预测精度涉及到电力工业投资的经济效益，超前投资造成资源浪费；滞后又影响经济发展．在上海居民生活用电预测中，用岭估计代替最小二乘估计，可以消除多元线性回归方程系数的估计为负带来的不合理结果，提高预测精度，本文总结了岭参数的选择对长期负荷预测的影响，取得较满意的预测结果．     

规划人口的动态灰色预测

本文运用灰色系统理论，提出了规划人口规模的灰色预测方法，并通过对某市人口的预测，验证了该方法的预测结果是较为符合实际的．     

一种新的昆虫神经网络预测预报方法

昆虫预测预报是植物保护中的一个困难问题．本文首先从系统的观点出发，分析了影响昆虫种群密度的主要因素；在研究传统时间序列模型共性的基础上，给出了广义时间序列模型；提出了一种新的昆虫预测模式—神经网络预测预报方法；用此方法对棉田害虫烟蓟马进行了预报，数值结果表明这是一种很好的预测预报方法，预测精度较其它方法都有很大提高．     

大气环境质量预测与评价的灰色定量刻划模型

本文针对大气环境系统中的灰色性，运用灰色系统理论对大气环境质量进行预测与评价．该理论与方法应用于临川市大气环境质量预测与评价中，结果令人满意．     

人力资源的预测方法

人力资源规划是必不可少的管理环节．科学的人力资源规划，必须建立在对人员供、需的准确预测的基础上．本文讨论人力资源预测的问题和转移矩阵等方法的使用．     

武安市二代棉铃虫卵发生量灰色超长期预测

本文运用灰色系统灾变预测理论，建立了武安市二代棉铃虫发生量预测模型．从而对武安市未来年份棉铃虫卵量进行预测．     

具有离散参数齐次随机场的线性预测(V)

本文提出并研究下列四类线性预测问题：2（n0），6（n0），5（NO，n0）及3（N0，n0）．不仅求出了它们的预测误差，而且也求出了它们的预测值．因而不仅推广了而且也改进了[2－5]中的结果．本文还给出了场｛X（m，n）｝的一个新形式的表示式．     

指数模型参数估计及其在通讯预测中的应用

本文提出了指数模型参数估计的一种方法，并应用指数模型对我国长途电话话务量进行预测．该预测方法较文献［１—２」中精确，从而说明此方法是可行的．     

组合预测方法在大气环境评价中的应用

李振亮．组合预测方法在大气环境评价中的应用．数理统计与管理，１９９７，１６（４），１２～１５．本文利用组合预测理论和方法，建立了一种大气环境质量评价的组合模型。实例分析表明，该组合评价模型具有利用各单一评价模型提供的信息和集中各单一模型的优点，是一种稳健和优化的环境质量评价模型。     

基于小波神经网络的中国能源需求预测模型

通过分析影响我国能源需求的主要因素,建立了基于小波神经网络的需求预测模型.采用定性与定量相结合的方式,分析了影响我国能源需求的主要因素,通过将人口总数、GDP、产业结构变化以及能源消费量的一阶滞后作为输入变量,建立基于小波神经网络的我国能源需求非线性预测模型.实验结果表明,该非线性预测模型与多元回归模型相比更加合理,具有更高的预测精度.     

区域经济发展智能预测方法

分析了影响区域经济发展的各种因素,指出由于这些因素相互制约、相互影响,使得传统的经济预测方法越来越难以胜任区域经济发展预测的需要.论述了核方法在处理非线性、不确定性和不精确性数据上存在的优势,建立了基于核方法三种经济预测模型,并将这三种预测模型与其它两种预测方法一起,对区域经济的发展进行组合预测.最后,采用数据融合的方法将各个体模型的预测结果进行集成,作为最终的输出.实际的结果表明,基于核方法的组合预测技术能取得较为理想的预测效果.     

Forecasting the demand for banknotes: some empirical results for the Netherlands

The desired production of banknotes is the product of the demand for banknotes at any particular time, and the average lifetime. While the latter might be relatively constant, the first shows a steady increase over time. Various techniques are available to generate forecasts of banknote demand. They have in common that they are extrapolative methods which assume that the future may be derived from the pattern of the past. One major class of forecasting methods of this type are time series models, which are particularly useful in a limited information environment. Causal or regression models provide another class of forecasting methods and are particularly valuable for annual or medium-term prediction, and in cases where explanatory variables are controlled by the policy maker. Contrary to these quantitative models, qualitative forecasting methods do not require as many data but are sometimes very beneficial in guiding intuitive thinking about future developments.The present paper presents some examples of forecasting models studied at the Netherlands Bank as well as some results obtained from qualitative research undertaken in the recent past.     

灰色预测模型的应用研究

根据灰色系统预测模型,给出和序列的多个预测方法,结合具体实例,分析各个方法的相对误差,找出相对误差最小的预测模型,从而确定实际问题的具体预测模型,同时总结各个方法的优劣;文中还根据和序列给出积序列、指数和对数序列灰色预测方法.     

Reviews on the Research of Dynamic Mortality Models

Mortality forecasting is the basis of population forecasting. In recent years, new progress has been made in mortality models. From the earliest static mortality models, mortality models have been developed into dynamic forecasting models including time terms, such as Lee-Carter model family, CBD model family and so on. This paper reviews and sorts out relevant literature on mortality forecasting models. With the development of dynamic models, some scholars have developed a series of mortality improvement models based on the level of mortality improvement. In addition, with the progress of mortality research, multi-population mortality modeling attracted the attention of researchers, and the multi-population forecasting models have been constantly developed and improved, which play an important role in the mortality forecasting. With the continuous enrichment and innovation of mortality model research methods, new statistical methods (such as machine learning) have been applied in mortality modeling, and the accuracy of fitting and prediction has been improved. In addition to the extension of classical modeling methods, issues such as small-area population or missing data of the population, the elderly population, the related population mortality modeling are still worth studying.     

最佳灰色回归组合模型及其在中国火灾预测中的应用

火灾每年给国家和人民生命财产造成巨大损失.火灾现象具有随机性、模糊性,是个复杂的灰色系统行为.研究火灾发生规律及发展趋势,具有实用价值.为此,首先给出最小二乘估计(LSE)意义下的最佳组合预测模型的定义,并求得组合模型的权的公式和证明权的唯一性.其次,用回归分析方法建立多个回归模型,并按以下三条标准:①回归指数(或相关系数)r大、②系统误差s小、③模型精度p高,选定最佳非线性回归模型;用灰色理论建立多个灰色模型,并按以下三条标准:①后验差比值c小、②小误差概率P大、③预测关联度ξ大,选定最佳灰色模型;再用最小二乘法将最佳回归模型与最佳灰色模型有机地结合起来建立的中国火灾最佳灰色回归组合预测模型.最佳灰色回归组合预测模型综合利用前两者提供的不同的有用信息,改善了单一模型的局限性,提高了模型的预测精度,减少了预测误差,使预测效果更佳.组合模型预测中国年火灾起数处于动态增长过程.     

Quantitative Forecasting — The State of the Art: Extrapolative Models

This paper sponsored by forecasting Study Group of the O.R. Society surveys the alternative extrapolative forecasting models regularly used and examines the various problems that arise. Extrapolative models are particularly appropriate when used for inventory control and materials purchase, and these two situations are considered. The paper concludes with an analysis of the evidence on the forecasting accuracy of extrapolative models.     

Mortality density forecasts: An analysis of six stochastic mortality models

This paper develops a framework for developing forecasts of future mortality rates. We discuss the suitability of six stochastic mortality models for forecasting future mortality and estimating the density of mortality rates at different ages. In particular, the models are assessed individually with reference to the following qualitative criteria that focus on the plausibility of their forecasts: biological reasonableness; the plausibility of predicted levels of uncertainty in forecasts at different ages; and the robustness of the forecasts relative to the sample period used to fit the model. An important, though unsurprising, conclusion is that a good fit to historical data does not guarantee sensible forecasts. We also discuss the issue of model risk, common to many modelling situations in demography and elsewhere. We find that even for those models satisfying our qualitative criteria, there are significant differences among central forecasts of mortality rates at different ages and among the distributions surrounding those central forecasts.     

霍尔特-温特模型在货运量季节性预测中的应用

为了提高货运量季节性预测的精度,应用了一种线性季节预测模型,霍尔特-温特模型。通过傅立叶周期分析确定季节影响和周期长度,利用迭代法寻找模型最优参数。将预测结果与其他三个常用季节预测模型:分组回归模型、可变季节预测模型和季节周期回归模型进行比较,结果表明霍尔特-温特模型预测精度最高。     

不等时距灰色组合预测模型的研究

在不等时距序列灰色预测的基础上,建立了最优组合预测模型和BP神经网络组合预测模型.通过实例对比分析,表明这两种灰色组合预测模型都较各单项模型具有更高的预测精度和较高的理论实践价值.