Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes

We introduce a new strategy for the prediction of linear temporal aggregates; we call it ‘hybrid’ and study its performance using asymptotic theory. This scheme consists of carrying out model parameter estimation with data sampled at the highest available frequency and the subsequent prediction with data and models aggregated according to the forecasting horizon of interest. We develop explicit expressions that approximately quantify the mean square forecasting errors associated with the different prediction schemes and that take into account the estimation error component. These approximate estimates indicate that the hybrid forecasting scheme tends to outperform the so‐called ‘all‐aggregated’ approach and, in some instances, the ‘all‐disaggregated’ strategy that is known to be optimal when model selection and estimation errors are neglected. Unlike other related approximate formulas existing in the literature, those proposed in this paper are totally explicit and require neither assumptions on the second‐order stationarity of the sample nor Monte Carlo simulations for their evaluation. Copyright © 2014 John Wiley & Sons, Ltd.     

创业投资辛迪加网络中控制权的演化博弈分析

创业投资的高风险性和信息不对称性使创业投资中控制权分配问题逐渐成为关系到风险投资项目成败的关键。本文运用演化博弈方法,分析控制权在辛迪加网络中两类创业投资家之间分配的博弈过程,得出在不同控制权分配比例下相应的演化稳定策略。在此结果上表明即期博弈中,创业投资家的私人收益和项目产出分配值的对比关系与控制权的分配密切相关。     

Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.     

基于小波分析与卡尔曼滤波的网络流量估计和预测方法

将目标状态的小波变换系数向量描述为卡尔曼滤波方法的状态变量,进而建立了网络流量估计和预测模型,能够实现周期内的实时跟踪和动态多步预测.利用CERNET华中地区主干网的实测流量数据对该模型进行检验,所有检验周期网络流量预测值的相对误差均值为4.58%,表明网络流量估计和预测模型具有较强的适用性.     

浅谈电力DSM技术及其在中国的发展

主要介绍了电力需求方管理或需求侧管理(DSM)的发展、目的和技术,论述了实施DSM的效益以及DSM在我国的实施情况和前景。     

基于多元回归法的气体浓度预测方法

从分析气体浓度预测的基本特性入手,设计与实现一种基于多元回归法的气体浓度预测方法.通过分析各种因素对预测结果的影响来选择预测的模型,将多元回归法应用到气体浓度的预测,以建立气体浓度预测模型,并采用图表的方式直观地显示预测结果.应用结果表明:该方法可以有效地对气体浓度进行预测,且预测精度可以满足实际需求.     

东海入海爆发性气旋预报系统

利用卫星云图资料,结合东海入海爆发性气旋的生成和发展先兆,对东海入海爆发性气旋的发生、发展和移动路径等要素的预报方法进行了分析,并建立了相应的预报系统。实际资料效果检验表明：该方法对东海入海爆发性气旋要素预报的准确度较高,所建系统使用方便、灵活,为东海入海爆发性气旋的预报提供了新思路和新方法。     

含山县土地利用结构优化研究

在分析含山县土地利用现状的基础上,结合该县社会经济发展需求和有关规划控制指标,运用灰色线性规划模型求解2010年高、中、低经济效益目标下土地利用结构优化方案,共获得三个供选方案,并运用AHP法分析每个方案的耦合效益,最后选择耦合效益最大者作为2010年土地利用结构优化推荐方案.     

面向MIS的预测支持系统的研究

通过对预测支持系统研究现状的分析,讨论了预测支持系统如何更好的适用于MIS,为其提供预测支持.并提出了更加适应MIS当今和未来发展的预测支持系统构建模式.     

时用水量预测的自适应组合动态建模方法

利用随机过程及时间序列分析手段,根据用水量序列季节性、趋势性及随机扰动性的特点,建立了用水量预测的自适应组合平滑模型。利用递推最小二乘算法及卡尔曼滤波算法解决了模型参数的动态估计问题。该法经实例验证,预测误差较小,可满足供水系统调度的实际需要。