有限交换环零因子图的邻接矩阵

研究了有限交换环的零因子图的邻接矩阵,对于任意素数 p、q确定了环Zp [i]× Zq [i]的零因子图的邻接矩阵的特征多项式的一些系数.     

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

The most up‐to‐date annual average daily traffic (AADT) is always required for transport model development and calibration. However, the current‐year AADT data are not always available. The short‐term traffic flow forecasting models can be used to predict the traffic flows for the current year. In this paper, two non‐parametric models, non‐parametric regression (NPR) and Gaussian maximum likelihood (GML), are chosen for short‐term traffic forecasting based on historical data collected for the annual traffic census (ATC) in Hong Kong. These models are adapted as they are more flexible and efficient in forecasting the daily vehicular flows in the Hong Kong ATC core stations (in total of 87 stations). The daily vehicular flows predicted by these models are then used to calculate the AADT of the current year, 1999. The overall prediction and comparison results show that the NPR model produces better forecasts than the GML model using the ATC data in Hong Kong. Copyright © 2006 John Wiley _ Sons, Ltd.     

应用SVM的三维车型识别技术

提出了一种新的车型识别技术.该技术分成两大部分,第一部分是如何准确地测量车辆的三维信息.首先需要从一系列图象中准确检测出车辆,然后从背景中将车辆完整的分割出来,再使用一个简单的透视变形模型,完成从二维信息到三维信息的转换,获得车辆的长度、宽度和高度特征.第二部分是将这些特征输入一个SVM分类器进行识别处理.实验证明,该方法在车型识别中,具有比较高的识别精度.     

基于支持向量机的有效带宽估计算法的研究

高性能网络设计的关键是对性能参数进行建模和估计的能力，通信业务在网络上传输时，其带宽要求与通信质量密切相关，合理分配带宽能很好提高网络性能，合理分配网络带宽的首要任务是能够根据网络信息的变化，预测带宽使用情况，为此，本文采用支持向量机（SVM）对网络业务流数据进行预测估计，通过训练样本，从而获得样本以外数据的分布规律，在此基础上，设计了一种网络传输有效带宽使用的估计算法，实验表明，该估计算法具有较高的训练效率和很高的估计精确度。     

Statistical surveillance of cyclical processes with application to turns in business cycles

On‐line monitoring of cyclical processes is studied. An important application is early prediction of the next turn in business cycles by an alarm for a turn in a leading index. Three likelihood‐based methods for detection of a turn are compared in detail. One of the methods is based on a hidden Markov model. The two others are based on the theory of statistical surveillance. One of these is free from parametric assumptions of the curve. Evaluations are made of the effect of different specifications of the curve and the transitions. The methods are made comparable by alarm limits, which give the same median time to the first false alarm, but also other approaches for comparability are discussed. Results are given on the expected delay time to a correct alarm, the probability of detection of a turning point within a specified time, and the predictive value of an alarm. Copyright © 2005 John Wiley & Sons, Ltd.     

A leading indicator approach to predicting short‐term shifts in demand for business travel by air to and from the UK

This paper uses the probit model to examine whether leading indicator information could be used for the purpose of predicting short‐term shifts in demand for business travel by air to and from the UK. Leading indicators considered include measures of business expectations, availability of funds for corporate travel and some well‐known macroeconomic indicators. The model performance is evaluated on in‐ and out‐of‐sample basis, as well as against a linear leading indicator model, which is used to mimic the current forecasting practice in the air transport industry. The estimated probit model is shown to provide timely predictions of the early 1980s and 1990s industry recessions and is shown to be more accurate than the benchmark linear model. Copyright © 2005 John Wiley & Sons, Ltd.     

Modelling trends in central England temperatures

Trends are extracted from the central England temperature (CET) data available from 1723, using both annual and seasonal averages. Attention is focused on fitting non‐parametric trends and it is found that, while there is no compelling evidence of a trend increase in the CET, there have been three periods of cooling, stability, and warming, roughly associated with the beginning and the end of the Industrial Revolution. There does appear to have been an upward shift in trend spring temperatures, but forecasting of current trends is hazardous because of the statistical uncertainty surrounding them. Copyright © 2003 John Wiley & Sons, Ltd.     

Combination forecasts of output growth in a seven‐country data set

This paper uses forecast combination methods to forecast output growth in a seven‐country quarterly economic data set covering 1959–1999, with up to 73 predictors per country. Although the forecasts based on individual predictors are unstable over time and across countries, and on average perform worse than an autoregressive benchmark, the combination forecasts often improve upon autoregressive forecasts. Despite the unstable performance of the constituent forecasts, the most successful combination forecasts, like the mean, are the least sensitive to the recent performance of the individual forecasts. While consistent with other evidence on the success of simple combination forecasts, this finding is difficult to explain using the theory of combination forecasting in a stationary environment. Copyright © 2004 John Wiley & Sons, Ltd.     

Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation

The growing affluence of the East and Southeast Asian economies has come about through a substantial increase in their economic links with the rest of the world, the OECD economies in particular. Econometric studies that try to quantify these links face a severe shortage of high‐frequency time series data for China and the group of ASEAN4 (Indonesia, Malaysia, Philippines and Thailand). In this paper we provide quarterly real GDP estimates for these countries derived by applying the Chow–Lin related series technique to annual real GDP series. The quality of the disaggregated series is evaluated through a number of indirect methods. Some potential problems of using readily available univariate disaggregation techniques are also highlighted. Copyright © 2004 John Wiley & Sons, Ltd.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.