基于马尔科夫分析的航材消耗预测

准确预测航材消耗量对保障飞行意义重大,航材消耗的特点符合马尔科夫过程。马尔科夫预测是关于事件发生概率预测的方法,通过示例分析表明在样本数据符合条件的情况下运用马尔科夫分析对航材消耗进行预测能够取得较好的预测效果。     

灰色预测在航材消耗预测上的应用

针对航材种类繁多、规格复杂、准确预测航材消耗比较困难的问题,提出利用灰色系统理论中的GM(1,1)模型,根据历年航材消耗数据,确定其预测模型。算例表明本模型具有较大的准确性和实用性。     

基于R语言SARIMA模型的航材需求预测分析

采用时间序列SARIMA模型对航材需求进行预测,以2010～2014年某航材实际月需求量数据为基础,运用R语言对航材需求量时间序列进行了稳定性判别;通过定阶和参数估计,构建了航材需求预测模型,并进行了数据预测。结果显示使用SARIMA模型拟合效果较好,预测能力可靠,能为航材部门需求预测提供准确方便的方法。     

无寿可修航材消耗定额模型

根据航材需求差均比大于、等于或小于1三种情况,提出负二项分布、泊松分布和二项分布三种需求分布。对服从泊松分布的二层级航材需求问题,采用负二项分布改进预测精确性。用满足率作为航材保障程度的衡量标准,并根据航材消耗定额的影响因素制定最低满足率。以给定的总保障经费和最低满足率为约束条件,总满足率最大为目标函数,建立航材消耗定额模型,采用边际分析法求解。算例证明本模型的预测效果良好。     

聚类分析在航材分类上的应用

针对航材种类繁多、规格复杂、消耗规律多样导致航材消耗研究较困难的问题,提出采用聚类分析方法根据历年航材消耗数据对大量航材进行分类,为避免量纲的影响采用马氏距离进行样本间相似性度量,为全面反映样本类间的相似性采用离差平方和进行类与类间的相似性度量。算例给出了利用聚类分析方法对一定量的样本进行分类的步骤和方法,结果表明分类效果显著。     

航材供应标准的基础性问题分析与研究

航材供应标准是航材保障单位制定航材订货计划和经费计划的基本依据,也是航材保障精确化管理的重要内容。本文针对航材消耗影响因素和航材故障与消耗数据这两个影响制定航材供应标准的基础性问题进行了分析研究,为科学制定航材供应标准提供了决策支持。     

航材备件需求预测模型与应用研究

分析了航材消耗与需求特点,从计算航材换件次数的角度进行分析,采用简单的函数拟合算法,给出需求规律拟合公式,结合具体实例进行了器材需求预测,并给出预测。计算结果表明,计算过程简单,拟合效果较好,易于工程采用。     

航材资源管理的研究进展:国内外文献综述

对航材资源的文献进行总结和梳理,对国内外相关研究进行了分析,并从航材预测、航材库存管理、航材供应链管理、航材仓库选址等方面进行了研究结果的总结与分析,提出了值得进一步研究的方向。     

HF飞机航材存贮模型研究

通过对HF飞机现状的分析.将航材分为动部件与消耗件,对航材的存贮策略进行了初步探讨。对航材收益与损失进行量化.利用边际分析法推导出最佳存贮量的求解模型.最后对模型进行了实例验证。     

航材消耗定额管理研究

针对航材消耗定额管理方法缺乏研究的问题,本文提出从航材消耗定额的制定与审批、执行、评估与修订等方面开展研究。本文对于航材保障进一步精确化、标准化具有重要参考价值。     

Online big data-driven oil consumption forecasting with Google trends

The rapid development of big data technologies and the Internet provides a rich mine of online big data (e.g., trend spotting) that can be helpful in predicting oil consumption — an essential but uncertain factor in the oil supply chain. An online big data-driven oil consumption forecasting model is proposed that uses Google trends, which finely reflect various related factors based on a myriad of search results. This model involves two main steps, relationship investigation and prediction improvement. First, cointegration tests and a Granger causality analysis are conducted in order to statistically test the predictive power of Google trends, in terms of having a significant relationship with oil consumption. Second, the effective Google trends are introduced into popular forecasting methods for predicting both oil consumption trends and values. The experimental study of global oil consumption prediction confirms that the proposed online big-data-driven forecasting work with Google trends improves on the traditional techniques without Google trends significantly, for both directional and level predictions.     

Forecasting retail sales using alternative models

This paper explores the issues associated with adapting forecasting techniques used by manufacturers to produce accurate forecasts for retail sales. A case study is presented that is developed using a retail situation because retailers often view their sales forecasting problems as being very different from a manufacturer's problems. Sales volumes are dramatically impacted by competitor promotional actions, discounts, store promotions and weather. Finally, consumption holidays like Christmas, Easter, Mother's day, have a large impact on sales as well as back to school shopping. The findings in this paper indicate that forecasting retail sales can be accomplished with a high degree of accuracy.     

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.     

基于灰色关联和回归分析的社会用电量预测研究

在电力市场化改革中,对社会用电量进行准确的预测十分必要。本文利用灰色关联分析理论对影响社会用电量的因素进行了筛选,再利用回归分析理论对社会用电量进行了预测。实例表明,灰色关联和回归分析理论应用于社会用电量预测是可行的。     

Consumer credit and consumption forecasts

Recent advances in the theory of consumer behavior indicate that consumption may exhibit non-linear dynamics characterized by occasional surges. Building upon them, and taking explicitly into account the forward-looking nature of consumption, this paper argues that rising consumer debt can signal such surges, as well as the consumption underprediction which will occur if they are not taken sufficiently into account in forecasting. This insight is tested with and strongly confirmed by the Organization of Economic Cooperation and Developments forecasts for the USA. The results should be of interest not only to professional forecasters and policy-makers, but also to theoretical economists and econometricians who study non-linear dynamic models.     

Demand forecasting and order planning for humanitarian logistics: An empirical assessment

Humanitarian aid organizations are most known for their short-term emergency relief. While getting aid items to those in need can be challenging, long-term projects provide an opportunity for demand planning supported by forecasting methods. Based on standardized consumption data of the Operational Center Amsterdam of Médecins Sans Frontières (MSF-OCA) regarding nineteen longer-term aid projects and over 2000 medical items consumed in 2013, we describe and analyze the forecasting and order planning process. We find that several internal and external factors influence forecast and order planning performance, be it indirectly through demand volatility and safety markup. Moreover, we identify opportunities for further improvement for MSF-OCA, and for humanitarian logistics organizations in general.     

基于组合模型的中长期电力负荷预测研究

本文研究了组合预测的模型,提高了预测的准确度。并对甘肃省2011-2020年全社会用电量做组合预测。     

安徽省能源需求的组合预测

根据预测理论,结合安徽省历年能耗的数据,在建立能源需求单项预测模型的基础上,建立组合预测模型,对2008～2014年的能源需求量进行预测。结果表明:组合预测模型的精度高于单项预测模型;安徽省能源需求量正以较快的速度在增长。     

基于BP神经网络的民用汽车运力灰色回归组合预测研究

文章根据组合预测的理论和BP神经网络对非线性数据良好的逼近特性。提出了基于BP神经网络的灰色预测、多项式回归模型的民用汽车运力组合预测模型。此模型综合了各单一模型的有效信息．能够比较客观地反映地区民用汽车运力的发展趋势．为相关部门提供决策依据。     

Sparse estimation of dynamic principal components for forecasting high-dimensional time series

We present the sparse estimation of one-sided dynamic principal components (ODPCs) to forecast high-dimensional time series. The forecast can be made directly with the ODPCs or by using them as estimates of the factors in a generalized dynamic factor model. It is shown that a large reduction in the number of parameters estimated for the ODPCs can be achieved without affecting their forecasting performance.