组合预测模型在我国能源消费系统中的建构及应用

组合预测理论及建模技术对于信息不完备的复杂经济系统具有一定的实用性 .鉴于能源消费系统的复杂性及非线性的特征 ,文章首先利用我国能源消费量的历史数据 ,分别采用灰色预测、神经网络及多元回归方法建立了我国能源消费系统的单项预测模型 .并对各单项模型的优缺点进行了比较分析 .其次 ,采用标准差法进行权重分配 ,建立了我国未来能源消费量的组合预测模型 .最后 ,应用该模型对我国未来 2 0年的能源消费量进行了预测 .结果表明 ,该模型可以作为我国未来能源消费量预测的有效工具 .     

Forecasting Hong Kong's container throughput: an error‐correction model

A three‐player oligopoly model is devised to capture the competitive interaction between operators of the Hong Kong container terminals, the Hong Kong midstream and the Singapore container terminals in providing container handling services. The oligopoly model is then estimated statistically and thereby the structural parameters can be identified. The results of the estimation confirm a substitutability between the services supplied by operators of different types (terminal versus midstream) and different locations (Hong Kong versus Singapore). Moreover, the model proposed in this article generates forecasts of demand for Hong Kong's container handling services that are more accurate than those reported by the government authority, and suggests an earlier construction of new terminals to meet future demand. Copyright © 2002 John Wiley & Sons, Ltd.     

Generalized exponential growth models a bayesian approach

A broad class of normal and non-normal models for processes with non-negative and non-decreasing mean function is presented. This class is called exponential growth models and the inferential procedure is based on dynamic Bayesian forecasting techniques. The aim is to produce the analysis on the original variable avoiding transformation and giving to the practitioner the opportunity to communicate easily with the model. This class of models includes the well-known exponential, logistic and Gompertz models. Models for counting data are compared with the Normal models using the appropriate variance law. In the examples, the novel aspects of this class of models are illustrated showing an improved performance over simple, standard linear models.     

Monetary policy,composite leading economic indicators and predicting the 2001 recession

On 26 November 2001, the National Bureau of Economic Research announced that the US economy had officially entered into a recession in March 2001. This decision was a surprise and did not end all the conflicting opinions expressed by economists. This matter was finally settled in July 2002 after a revision to the 2001 real gross domestic product showed negative growth rates for its first three quarters. A series of political and economic events in the years 2000–01 have increased the amount of uncertainty in the state of the economy, which in turn has resulted in the production of less reliable economic indicators and forecasts. This paper evaluates the performance of two very reliable methodologies for predicting a downturn in the US economy using composite leading economic indicators (CLI) for the years 2000–01. It explores the impact of the monetary policy on CLI and on the overall economy and shows how the gradualness and uncertainty of this impact on the overall economy have affected the forecasts of these methodologies. It suggests that the overexposure of the CLI to the monetary policy tools and a strong, but less effective, expansionary money policy have been the major factors in deteriorating the predictions of these methodologies. To improve these forecasts, it has explored the inclusion of the CLI diffusion index as a prior in the Bayesian methodology. Copyright © 2004 John Wiley & Sons, Ltd.     

控制系统中PID预测方法的实质研究

控制系统的滞后是难免的,滞后系统的预测理论和方法研究一直是控制工程中的重要课题.本文剖析了工业中常用的比例-积分-微分(PID)预测方法的实质,指出 PID 调节器是一个按二阶自回归模型进行控制的系统.笔者接着分析了 PID 调节器的预测误差,高于二阶的系统用 PID 方法预测时误差很大,从理论上得不到良好的控制效果.PID 只能适用于某些特定场合,文中探讨了使用方法.     

测井解释中的人工神经网络方法应用研究

研究利用人工神经网络方法进行测井解释中有关储层参数的测算和油层识别,并实际地应用于我国某油田的测井解释问题中.应用表明,此方法在测井解释问题上有广阔的应用前景.     

Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts

A decomposition of the Brier skill score shows that the performance of judgmental forecasts depends on seven components: environmental predictability, fidelity of the information system, match between environment and forecaster, reliability of information acquisition, reliability of information processing, conditional bias, and unconditional bias. These components provide a framework for research on the forecasting process. Selected literature addressing each component is reviewed, and implications for improving judgmental forecasting are discussed.     

Cointegration,error-correction models,and forecasting using realigned foreign exchange rates

This study employs error-correction models (ECMs) to forecast foreign exchange (FX) rates where the data-sampling procedures are consistent with the rules governing the settlement (delivery) of FX contracts in the FX market. The procedure involves thatching (aligning) the forward rate to the 'actual' realized (future) spot rate at the settlement (delivery) date. This approach facilitates the generation of five different sets of sub samples of FX rate series for each currency. For comparative purposes, non-aligned month-end rates are also examined. The results indicate that the moments of the realized forecast errors for the same currency are not similar. Further, the ECMs derived are unstable, and their forecasting performance vary. The forecasting performance of the ECMs appear to be affected by the choice of the interval in which the sets of sub samples are observed. These results are attributed to the observed seasonal variation in FX rates.     

思想政治工作应正视人的需要

需要是人的行为积极性的心理基础，也是人的行为的原动力。笔者从心理学角度出发，在分析、论述了人的需要的现实意义及其特点、种类、层次的基础上，提出了解决人的需要问题的基本原则和方法。     

Technological forecasting with nonlinear models

The S-shaped growth curves such as Gompertz, logistic, normal and Weibuli are widely used for forecasting technological substitutions. A family of data-based transformed (DBT) models, which are linear in the regression parameters, including the above-mentioned four models as special cases has been shown to be quite useful for short-term forecasts. This paper explores modeling the technology penetration data directly with assumed S-shaped growth curves. The resulting models, which are nonlinear in the regression parameters, also incorporate proper dependence structure and power transformation. It appears that the nonlinear modeling is a viable alternative to the DBT and other conventional forecasting models in forecasting technological substitutions. Hence, an appropriate strategy is to consider the nonlinear modeling approaches as possible alternatives and use the data at hand to select, via pseudo-cross-validation, the best model for forecasting purposes.