组合灰色预测模型对物流需求预测效果的分析

在线性回归与GM(1,1)组合模型对物流需求量预测,可取得优于单方法预测效果的前提下,将线性回归法运用到与DGM(1,1)模型进行组合,检测该组合灰色预测模型对物流需求量预测效果。通过对国内2001-2012年物流需求量历史数据的分析,发现组合灰色预测模型的预测效果优于线性回归模型,但低于DGM(1,1)模型,最后利用线性回归模型、DGM(1,1)模型、简单平均组合模型和组合灰色预测模型对国内2013-2017的物流需求量进行了有效预测。     

组合灰色预测模型对物流需求预测效果的分析

在线性回归与GM（1,1）组合模型对物流需求量预测,可取得优于单方法预测效果的前提下,将线性回归法运用到与DGM（1,1）模型进行组合,检测该组合灰色预测模型对物流需求量预测效果。通过对国内2001-2012年物流需求量历史数据的分析,发现组合灰色预测模型的预测效果优于线性回归模型,但低于DGM（1,1）模型,最后利用线性回归模型、DGM（1,1）模型、简单平均组合模型和组合灰色预测模型对国内2013-2017的物流需求量进行了有效预测。     

基于IOWA算子的灰色—多项式组合模型在武汉市物流需求预测中的应用

针对2002～2009年武汉市物流需求的数据,采用灰色GM 1,1模型和多项式拟合模型两种单项预测模型对数据进行建模预测。并结合组合预测理论,采用基于IOWA算子的组合预测模型进行预测,结果表明基于诱导有序加权平均算子的组合预测模型的预测精度明显高于两种单项预测方法,说明了该方法用于物流需求预测的可行性和有效性,并在此基础上对2010～2012年的武汉市物流需求作出预测。     

基于IOWA算子的组合预测模型在物流需求预测中的应用

根据2000~2009年宁波市物流需求的数据,采用灰色GM 1,,1,模型和一元线性回归模型进行组合优化,建立了基于诱导有序加权平均(IOWA)算子的物流需求量组合预测模型。结果表明基于IOWA算子的组合预测模型能有效提高预测精度,说明了该方法用于物流需求预测的可行性和有效性,并在此基础上对2010~2013年宁波市物流需求作出预测。     

基于流程的军事物流供应链运作模型研究

本文阐述了军事物流供应链运作模型研究的意义,总结当前军事物流供应链运作模型的两种基本类型：Y状军事物流供应链运作模型和X状军事物流供应链运作模型.通过对这两种基本模型的比较研究,提出基于流程的军事物流供应链标准运作模型.     

基于组合灰色预测模型的物流企业运营成本预测

运营成本预测是物流企业制定企业发展战略的基础。文章基于GM(1,1)模型、Verhulst模型和SCGM(1,1)c模型建立组合灰色预测模型,运用预测有效度方法确定组合预测模型的权重系数,对物流企业运营成本进行预测。选用P物流企业2000—2009年的运营成本实际值作为原始数据,利用各预测模型预测2010—2012年物流企业运营成本。预测结果表明,组合灰色预测模型比单一预测模型具有更高的预测精度。在验证组合灰色预测模型可行性的基础上,进一步预测物流企业2013—2017年运营成本,为成本预测及相关领域提供理论及方法借鉴。     

构建基于数据挖掘技术的军事物流信息平台

着眼于军事物流发展趋势,针对来．采战争对军事物流提出的新要求,在剖析当前军事物流信息平台弊端的基础上,提出了基于DM技术的军事物流信息平台,并对该平台的优势进行了详细论述。     

美军物流系统的变革方略(六)定单驱动:美军物流系统的运作流程规划

1引言基于配送的军事物流系统的运作都是围绕军事用户的物资需求而进行的,因此,为军事用户提供满意的物资保障服务是基于配送的军事物流系统运作的目标。这种保障方式称之为配送式保障。配送式保障的核心是根据精确预测军事用户的物资需求,通过实时获取物资信息,灵活调整军事物流资源实体     

适应高技术战争需要,加快我军装备物流建设

强大的军事物流体系已经成为高技术战争强有力的支撑。军事物流的能力和水平，已经成为决定战争进程和结果的关键性制约因素。本文从高技术战争的特点和发展趋势出发，提出了高技术战争对军事物流的需求；基于对美军军事物流的分析，对装备物流的基本内涵、装备物流建设的发展战略和需要重视的问题进行了初步的探讨。     

国内外军事物流现状与发展研究

我军军事物流起步晚,现有军事物流与国外发达国家相比,在各环节都有明显差距。目的:为寻找我军军事物流短板,制定发展对策;方法:本文结合我国地形、周边战争局势等因素,对我国军事物流现状进行了分析,并深入研究了美俄两国先进的军事物流模式;结果:针对国外发展经验提出了我国军事物流发展思路;结论:该研究为发展我国军事物流技术提供了有价值的参考。     

Forecasting with approximate dynamic factor models: The role of non-pervasive shocks

This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.     

基于农村物流需求量的组合预测分析

农村物流需求量的预测对于农村物流的发展有重要意义。文章把农村消费品零售总额作为农村物流需求量预测指标．通过分析各影响因素,建立了多元回归、双指数平滑及移动平均单预测模型。根据得出的单项预测误差数据,采用折扣系数法建立组合预测模型,使得组合预测模型预测误差平方和最小。预测能力明显优于单项预测模型。     

基于ARIMA模型的我国电力生产预测研究

考虑到电力对国家社会经济发展的重要性,本文研究了我国电力生产的预测模型。鉴于我国电力生产时间序列数据的完备性,在对该时间序列进行平稳性检验的基础上,比较分析建立了我国电力生产量的ARIMA模型,并运用所建立模型进行了预测。     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

Forecasting performance of multivariate time series models with full and reduced rank: an empirical examination

Since Quenouille's influential work on multiple time series, much progress has been made towards the goal of parameter reduction and model fit. Relatively less attention has been paid to the systematic evaluation of out-of-sample forecast performance of multivariate time series models. In this paper, we update the hog data set studied by Quenouille (and other researchers who followed him). We re-estimate his model with extended observations (1867–1966), and generate recursive one- to four-steps-ahead forecasts for the period of 1967 through 2000. These forecasts are compared to forecasts from an unrestricted vector autoregression, a reduced rank regression model, an index model and a cointegration-based error correction model. The error correction model that takes into account both nonstationarity of the data and rank reduction performs best at all four forecasting horizons. However, differences among competing models are statistically insignificant in most cases. No model consistently encompasses the others at all four horizons.     

The effect of parameter uncertainty on forecast variances and confidence intervals for unit root and trend stationary time-series models

In this paper I describe the effect of parameter uncertainty on the way conditional forecast variances grow as the forecast horizon increases. Without parameter uncertainty, forecast variances for the unit root model grow linearly with the forecast horizon while with the trend stationary model they are bounded. With parameter uncertainty, however, I find that for both the unit root and the trend stationary models, forecast variances grow with the square of the forecast horizon so that uncertainty grows at a much faster rate than without parameter uncertainty.     

Forecasting combination and encompassing tests

In this paper we demonstrate that forecast encompassing tests are valuable tools in getting an insight into why competing forecasts may be combined to produce a composite forecast which is superior to the individual forecasts. We also argue that results from forecast encompassing tests are potentially useful in model specification. We illustrate this using forecasts of quarterly UK consumption expenditure data from three classes of models: ARIMA, DHSY and VAR models.     

Forecast combination for U.S. recessions with real-time data

This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index, as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use log-score and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.     

面向售后服务的汽车备品需求预测研究

根据面向售后服务的汽车备品需求特点的差异,本文将其分为专用配件和通用配件并分别选用不同的预测模型.对专用配件,采用基于时间序列相关的线性回归模型,并运用加权最小二乘法（WLS）估计参数.对通用配件,选用GM（1,1）模型进行需求预测.     

FORECASTING DISCONNECTED EXCHANGE RATES

The inability of empirical models to forecast exchange rates has given rise to the belief that exchange rates are disconnected from macroeconomic fundamentals. This paper addresses the potential disconnect by endogenously selecting forecast models from a broad set of fundamentals. The procedure shows that exchange rates are not disconnected from fundamentals, but fundamentals vary in their predictive content at different forecast horizons and for different currencies. Performing model selection out‐of‐sample is challenging. At short horizons, the method cannot outperform a random walk, although the performance is improved at long horizons. These findings are confirmed across currencies and forecast evaluation methods. Copyright © 2013 John Wiley & Sons, Ltd.