星敏感器星点聚类提取方法

星敏感器现有的星点提取方法运算时间长、存储需求大,从而限制星敏感器姿态初始捕获时间、轻小化等性能指标的提高.针对以上问题,提出了一种星敏感器星点聚类提取方法.首先,介绍和分析了星点聚类提取方法中的预处理滤波算法和聚类算法;然后,给出了该方法在北京控制工程研究所新研小型星敏感器中的实现方式和实现效果,该星敏感器在探月三期月地高速再入返回飞行器中成功实现了首次在轨飞行实验;最后,通过星敏感器观星实验对星点传统提取方法和星点聚类提取方法进行了比对验证,并通过小型星敏感器在轨飞行实验对星点聚类提取方法进行了在轨验证.实验显示,该方法提取星点所需时间最大约为传统方法的16%,星图存储需求不到传统方法的1%,且星点提取正确,在轨表现良好.结果表明,星敏感器星点聚类提取方法能有效减小星点提取所需时间,不需大容量星图存储器件,且逻辑简单、移植性好,适用于各类光学成像敏感器.     

一种结构自适应的聚类神经网络

为了解决聚类分析中聚类数的确定问题，在SOFM神经网络的基础上，从聚类准则出发，通过试验对聚类准则的曲线特征进行了详细的分析和论证，设计出一种结构自适应的聚类神经网络，该网络能自动确定最佳的聚类数，并提出了一种减少计算量的改进算法。     

模糊联想记忆规则提取的聚类方法

针对现有模糊联想记忆规则提取的聚类分析算法存在的问题，阐明了规则遗漏对模糊联想记忆性能的影响，提出糊联想记忆规则提取的新方法。新方法大大降低了聚类的难度和工作量，能够准确而迅速地提取FAM系统的规则，克服了现有方法的不足。     

面向数据流的加权聚类及演化分析研究

为解决无限数据流在有限内存空间中的聚类分析问题，本文提出了一种加权聚类及演化分析框架。为简要地描述此框架，给出了聚类、聚类簇的概念及其数据结构定义，接着对聚类、聚类簇的加法运算和差运算给出了清晰的描述和相应的实现算法。本框架与CluStream框架有较大的差别，这里采用聚类簇的加法运算来实现更大时间跨度内的聚类簇融合，采用聚类簇的差运算来进行聚类簇的演化分析。最后通过第一个例子来说明本框架是如何对数据流进行加权聚类及演化分析的，采用第二个例子来验证为实现本框架所需的十五个算法的正确性及有效性。     

基于模糊时间序列的国内旅游需求预测

在分析了大量经典模糊时问序列模型的基础上,将其引入国内旅游需求预测,指出了其在区间划分上存在的问题。结合聚类算法的优点对区间划分进行处理,提出了基于改进聚类算法的模糊时间序列模型对国内旅游需求人数加以预测。研究结果表明,该模型在保证较高预测精度的前提下,简化了计算,且易于理解,具有较好的应用价值。     

基于谱聚类的图像多尺度随机树分割

针对谱聚类（spectral clustering）应用于图像分割时权矩阵的谱难以计算的实际问题，定义了像素点与类之间的距离，给出一个采样数定理，设计了一个图像的分层分割（hierarchical divisive）算法．在利用该算法进行图像分割时，由于既要对待分类的点进行随机抽样，又要通过调节尺度因子来合并较小的类或拆分较大的类，因此图像的分割既具有随机性又具有多尺度特性，称之为基于谱聚类的图像多尺度随机树分割（multiscale stochastic hierarchical image segmentation byspectral clustering，简写为MSHISSC）．实验结果表明了算法的有效性．     

基于模块性指标优化的层次聚类算法

层次形成的正确性决定了层次聚类的质量，通常围绕对象类内类间关系评价实现。本文基于聚类目标，综合考虑类内类问关系，借鉴网络分析中模块性评价准则，设计用于层次聚类的模块性指标，并采用自底向上合并的途径实现指标优化从而完成聚类，提出一种基于模块性指标优化的层次聚类算法。仿真试验表明，和谱聚类算法相比，本文介绍的算法实现简单，能以较少的计算代价，准确地获得样本特征，实现聚类。     

基于改进K均值的脸形聚类算法

针对海量人脸图像数据库检索时顺序匹配速度慢等问题,提出把聚类技术应用于数据库预分类,利用脸形特征对人脸图像自动聚类.首先用改进主动形状模型提取脸形特征,再用改进K-均值算法对人脸图像进行聚类.使用Hausdorff距离计算两个特征点集的相似度.实验表明,该算法的聚类结果比较稳定、精确且符合人类视觉认知特性.     

改进的等斜率灰色聚类法在地面水质量评价中的应用与分析

为了更好地将等斜率灰色聚类法应用于地表水质评价，提出了改进的等斜率灰色聚类法——灰色聚类样点排序法，并通过实例的计算比较，讨论灰色聚类样点排序法再权重处理过程的可行性。可以得出灰色聚类样点排序法能兼顾到：1）各测点地实测污染浓度都在级别标准范围内较有规律的变化，各污染物的标准之间差异不太大；2）污染物分布的离散度太大，各标准值之间差别也太大这两种情况。     

基于样本密度的非监督动态改进FCM算法

针对传统FCM算法中聚类结果对初始聚类中心敏感,分类数C依赖于先验知识确定的不足,提出了基于样本密度的非监督动态改进FCM算法。该算法基于初始样本密度确定初始聚类中心,基于模糊伪F统计量自动确定最佳分类数,实现了基于样本密度非监督状态下的动态聚类。选取瓦斯涌出量的聚类分析进行应用,应用结果表明,该算法能合理选择初始聚类中心并动态确定分类数,降低了对初始聚类中心的依赖度,提高了收敛速度和自动化程度,并能根据指标做出正确预测。     

The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR

As a representative emerging financial market, the Chinese stock market is more prone to volatility because of investor sentiment. It is reasonable to use efficient predictive methods to analyze the influence of investor sentiment on stock price forecasting. This paper conducts a comparative study about the predictive performance of artificial neural network, support vector regression (SVR) and autoregressive integrated moving average and selects SVR to study the asymmetry effect of investor sentiment on different industry index predictions. After studying the relevant financial indicators, the results divide the Shenwan first-class industries into two types and show that the industries affected by investor sentiment are composed of young companies with high growth and high operative pressure and there are a great number of investment bubbles in those companies.     

中小科技企业在香港创业板上市需特别重视的几个问题

中小科技企业由于自身的一些特点，如创建初期的风险较高、自身的资产规模较小、经营体制不健全、盈利能力差等，加上现阶段我国又缺乏必要的信用担保体系等的限制，使得其获得国有商业银行贷款的可能性不大。在这种情况下，众多中小科技企业寄希望于直接融资这一渠道上。本文试对中小科技企业在香港创业板市场上市筹备中的难点问题进行深入分析，希望有助于更多的科技企业成功上市。     

高管股权激励对R&D资本化与费用化价值相关性的调节效应

选取2007~2011年披露研发投入且数据完整的1695家上市公司为研究样本,利用层次回归分析,实证检验了高管股权激励对RD资本化与费用化价值相关性的影响作用。研究结果表明:高管股权激励不仅是RD资本化投入与股票价格的半调节变量,同时,高管股权激励也是RD费用化投入与股票价格的半调节变量;通过高管股权激励,可以解决监管RD活动中信息不对称和风险性等问题,使企业实现股东价值最大化。     

International equity flows and the predictability of US stock returns

We examined the link between international equity flows and US stock returns. Based on the results of tests of in‐sample and out‐of‐sample predictability of stock returns, we found evidence of a strong positive (negative) link between international equity flows and contemporaneous (one‐month‐ahead) stock returns. Our results also indicate that an investor, in real time, could have used information on the link between international equity flows and one‐month‐ahead stock returns to improve the performance of simple trading rules. Copyright © 2007 John Wiley & Sons, Ltd.     

Integrating time-series and end-use methods to forecast electricity sales

Two types of forecasting methods have been receiving increasing attention by electric utility forecasters. The first type, called end-use forecasting, is recognized as an approach which is well suited for forecasting during periods characterized by technological change. The method is straightforward. The stock levels of energy-consuming equipment are forecast, as well as the energy consumption characteristics of the equipment. The final forecast is the product of the stock and usage characteristics. This approach is well suited to forecasting long time periods when technological change, equipment depletion and replacement, and other structural changes are evident. For time periods of shorter duration, these factors are static and variations are more likely to result from shocks to the environment. The shocks influence the usage of the equipment. A second forecasting approach using time-series analysis has been demonstrated to be superior for these applications. This paper discusses the integration of the two methods into a unified system. The result is a time-series model whose parameter effects become dynamic in character. An example of the models being used at the Georgia Power Company is presented. It is demonstrated that a time-series model which incorporates end-use stock and usage information is superior—even in short-term forecasting situations—to a similar time-series model which excludes the information.     

Evaluating predictive performance of value‐at‐risk models in emerging markets: a reality check

We investigate the predictive performance of various classes of value‐at‐risk (VaR) models in several dimensions—unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the conditional distribution function. By using the reality check test of White (2000), we compare the predictive power of alternative VaR models in terms of the empirical coverage probability and the predictive quantile loss for the stock markets of five Asian economies that suffered from the 1997–1998 financial crisis. The results based on these two criteria are largely compatible and indicate some empirical regularities of risk forecasts. The Riskmetrics model behaves reasonably well in tranquil periods, while some extreme value theory (EVT)‐based models do better in the crisis period. Filtering often appears to be useful for some models, particularly for the EVT models, though it could be harmful for some other models. The CaViaR quantile regression models of Engle and Manganelli (2004) have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the three periods before, during and after the crisis. Copyright © 2006 John Wiley & Sons, Ltd.     

Dynamic relationship analysis of us gasoline and crude oil prices

This paper studies the dynamic relationships between US gasoline prices, crude oil prices, and the stock of gasoline. Using monthly data between January 1973 and December 1987, we find that the US gasoline price is mainly influenced by the price of crude oil. The stock of gasoline has little or no influence on the price of gasoline during the period before the second energy crisis, and seems to have some influence during the period after. We also find that the dynamic relationship between the prices of gasoline and crude oil changes over time, shifting from a longer lag response to a shorter lag response. Box-Jenkins ARIMA and transfer function models are employed in this study. These models are estimated using estimation procedure with and without outlier adjustment. For model estimation with outlier adjustment, an iterative procedure for the joint estimation of model parameters and outlier effects is employed. The forecasting performance of these models is carefully examined. For the purpose of illustration, we also analyze these time series using classical white-noise regression models. The results show the importance of using appropriate time-series methods in modeling and forecasting when the data are serially correlated. This paper also demonstrates the problems of time-series modeling when outliers are present.     

Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH-MIDAS and deep learning models

Empirical experiments have shown that macroeconomic variables can affect the volatility of stock market. However, the frequencies of macroeconomic variables are low and different from the stock market volatility, and few literature considers the low-frequency macroeconomic variables as input indicators for deep learning models. In this paper, we forecast the stock market volatility incorporating low-frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH-MIDAS) model to process the mixing frequency data. This paper firstly takes macroeconomic variables as exogenous variables then uses the GARCH-MIDAS model to deal with the problem of different frequencies between the macroeconomic variables and stock market volatility and to forecast the short-term volatility and finally takes the predicted short-term volatility as the input indicator into machine learning and deep learning models to forecast the realized volatility of stock market. It is found that adding macroeconomic variables can significantly improve the forecasting ability in the comparison of the forecasting effects of the same model before and after adding the macroeconomic variables. Additionally, in the comparison of the forecasting effects among different models, it is also found that the forecasting effect of the deep learning model is the best, the machine learning model is worse, and the traditional econometric model is the worst.     

Combining ordinal forecasts with an application in a financial market

The literature on combining forecasts has almost exclusively focused on combining point forecasts. The issues and methods of combining ordinal forecasts have not yet been fully explored, even though ordinal forecasting has many practical applications in business and social research. In this paper, we consider the case of forecasting the movement of the stock market which has three possible states (bullish, bearish and sluggish). Given the sample of states predicted by different forecasters, several statistical and operation research methods can be applied to determine the optimal weight assigned to each forecaster in combining the ordinal forecasts. The performance of these methods is examined using Hong Kong stock market forecasting data, and their accuracies are found to be better than the consensus method and individual forecasts.     

Moment tests for density forecast evaluation in the presence of parameter estimation uncertainty

Density forecast (DF) possesses appealing properties when it is correctly specified for the true conditional distribution. Although a number of parametric specification tests have been introduced for the DF evaluation (DFE) in the parameter‐free context, econometric DF models are typically parameter‐dependent. In this paper, we first use a generalized probability integral transformation‐based moment test to unify these existing tests, and then apply the Newey–Tauchen method (the West–McCracken method) to correct this unified test as a generalized full‐sample (out‐of‐sample) test in the parameter‐dependent context. Unlike the corrected tests, the uncorrected tests could be substantially undersized (oversized) when they are directly applied to the full‐sample (out‐of‐sample) DFE in the presence of parameter estimation uncertainty. We also use a simulation to show the usefulness of the corrected tests in rectifying the size distortion problem, and apply the corrected tests to an empirical study of stock index returns. Copyright © 2010 John Wiley & Sons, Ltd.