Different Approaches to Forecast Interval Time Series: A Comparison in Finance

An interval time series (ITS) is a time series where each period is described by an interval. In finance, ITS can describe the temporal evolution of the high and low prices of an asset throughout time. These price intervals are related to the concept of volatility and are worth considering in order to place buy or sell orders. This article reviews two approaches to forecast ITS. On the one hand, the first approach consists of using univariate or multivariate forecasting methods. The possible cointegrating relation between the high and low values is analyzed for multivariate models and the equivalence of the VAR models is shown for the minimum and the maximum time series, as well as for the center and radius time series. On the other hand, the second approach adapts classic forecasting methods to deal with ITS using interval arithmetic. These methods include exponential smoothing, the k-NN algorithm and the multilayer perceptron. The performance of these approaches is studied in two financial ITS. As a result, evidences of the predictability of the ITS are found, especially in the interval range. This fact opens a new path in volatility forecasting.     

四种通视性分析方法与实验比较

对于计算威胁区通视性的四种方法进行了分析与实验比较。所谓通视性分析（Ｖｉｓｉｂｉｌｉｔｙａｎａｌｙｓｉｓ）就是分析在三维空间中观察者发现目标的概率。通视性分析是飞行航路规划的基础,对于选择一条安全的航路具有重要意义。通视性分析的主要任务是找出使飞行器能够躲避给定的观察者,如雷达站、空中预警飞机和拦截飞机等区域。在这些区域中,飞行器具有最小被发现概率。选择那些使飞行器具有最小被发现概率的航路,可以大大增强飞行器的生存概率。文中讨论了四种通视性方法即点到区域法、快速点到三角形法、次快速点到三角形法、消隐法,并进行了实验比较。     

一种稳定的人脸轮廓模型

用Harris算子提取被测人脸上受表情影响较小的几个关键特征点,由这些特征点的相对座标位置与正面人脸模型中对应点的相对座标位置之间的转化关系估计出被测人脸的姿态向量,用此姿态向量与提取的点建立一个综合人脸轮廓模型。由于所提取的特征点受人脸表情变化影响很小,在模型中又正确估计了人脸姿态,因此,此模型可以很好地抑制人脸表情和姿态变化的影响,适合于作为识别人脸的依据。     

Tests of Random Walk: A Comparison of Bootstrap Approaches

This paper compares different versions of the multiple variance ratio test based on bootstrap techniques for the construction of empirical distributions. It also analyzes the crucial issue of selecting optimal block sizes when block bootstrap procedures are used. The comparison of the different approaches using Monte Carlo simulations leads to the conclusion that methodologies using block bootstrap methods present better performance for the construction of empirical distributions of the variance ratio test. Moreover, the results are highly sensitive to methods employed to test the null hypothesis of random walk.     

Splitting Finite Default Theories: A Comparison of Two Approaches

Default logic is computationally expensive. One of the most promising ways of easing this problem and developing powerful implementations is to split a default theory into smaller parts and compute extensions in a modular, local way. This paper compares two recent approaches, Turner's splitting and Cholewinski's stratification. It shows that the approaches are closely related – in fact the former can be viewed as a special case of the latter.     

Overview of the Geometries of Shape Spaces and Diffeomorphism Groups

This article provides an overview of various notions of shape spaces, including the space of parametrized and unparametrized curves, the space of immersions, the diffeomorphism group and the space of Riemannian metrics. We discuss the Riemannian metrics that can be defined thereon, and what is known about the properties of these metrics. We put particular emphasis on the induced geodesic distance, the geodesic equation and its well-posedness, geodesic and metric completeness and properties of the curvature.     

图像分类方法比较研究

近年来随着多媒体技术的发展，图像分类技术受到普遍的关注，且采用的方法以机器学习方法为主。但由于图像媒体的特殊性，在关系数据或文本数据中比较成功的分类算法应用到图像数据时，分类准确率往往比较低。我们选择了几个常用的分类算法，通过分析比较它们应用于不同图像特征时的分类结果，以此发现各个算法在解决图像分类的不足之处，并针对不足之处提出图像分类的改进思想。     

改进的ASM方法在人脸定位中的应用

在传统ASM模型的基础上，采用以轮廓点为中心的矩形区域的DCT系数进行轮廓匹配，提出了一种改进算法。该方法充分利用了轮廓点附近的二维纹理信息，改善了ASM方法采用轮廓点附近一维灰度信息导致模型匹配精度不高的问题。DCT 具有良好的去相关性能和能量集中性能，仅采取3.52%的DCT系数却平均保留了图像95%的能量，并采用八方向搜索最佳轮廓点，提高了定位速度。实验证明该方法非常有效。     

基于不同视角的人脸模型识别方法

对在不同视角下,得到的人脸模型,文中提出一种基于人脸表面的识别方法,采用平面射影变换,将人脸的图像变换到一个相同的图像,使图像对齐;而后运用主成分分析法(PCA)进行分类。基于这种方法,由光线、面部表情、姿态的改变引起的不必要变化就可能被消除或可以忽略。这种方法可以达到比较准确的识别人脸的目的。实验结果显示,文中的方法对人脸模型提供了更好的表达,并且人脸识别的错误率更低。     

基于不同视角的人脸模型识别方法

对在不同视角下，得到的人脸模型，文中提出一种基于人脸表面的识别方法，采用平面射影变换，将人脸的图像变换到一个相同的图像，使图像对齐；而后运用主成分分析法（PCA）进行分类。基于这种方法，由光线、面部表情、姿态的改变引起的不必要变化就可能被消除或可以忽略。这种方法可以达到比较准确的识别人脸的目的。实验结果显示，文中的方法对人脸模型提供了更好的表达，并且人脸识别的错误率更低。     

A Review of Statistical Approaches to Level Set Segmentation: Integrating Color,Texture, Motion and Shape

Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.     

A Comparison of Inferences about Containers and Surfaces in Small-Scale and Large-Scale Spaces

Inference mechanisms about spatial relations constitute an important aspect of spatial reasoning as they allow users to derive unknown spatial information from a set of known spatial relations. When formalized in the form of algebras, spatial-relation inferences represent a mathematically sound definition of the behavior of spatial relations, which can be used to specify constraints in spatial query languages. Current spatial query languages utilize spatial concepts that are derived primarily from geometric principles, which do not necessarily match with the concepts people use when they reason and communicate about spatial relations. This paper presents an alternative approach to spatial reasoning by starting with a small set of spatial operators that are derived from concepts closely related to human cognition. This cognitive foundation comes from the behavior of image schemata, which are cognitive structures for organizing people's experiences and comprehension. From the operations and spatial relations of a small-scale space, a container–surface algebra is defined with nine basic spatial operators—inside, outside, on, off, their respective converse relations—contains, excludes, supports, separated_from, and the identity relation equal. The container–surface algebra was applied to spaces with objects of different sizes and its inferences were assessed through human-subject experiments. Discrepancies between the container–surface algebra and the human-subject testing appear for combinations of spatial relations that result in more than one possible inference depending on the relative size of objects. For configurations with small- and large-scale objects larger discrepancies were found because people use relations such as part of and at in lieu of in. Basic concepts such as containers and surfaces seem to be a promising approach to define and derive inferences among spatial relations that are close to human reasoning.     

Business Model Innovation: Propositions on the Appropriateness of Different Learning Approaches

Firms are often faced with market and technological uncertainty when trying to renew their business model. The literature suggests that under uncertainty, firms can either develop new business models through commitment, incremental experimentation, or radical experimentation. Experience effects, complexity, and ambiguity influence the appropriateness of these learning approaches. However, no overarching view exists on whether and how these factors interact. Based on a simulation model, we develop propositions regarding the combined influences of complexity, ambiguity, and experience effects on the performance of these learning approaches. We also find that the firm's time perspective matters. The results allow us to refine existing theoretical logic and to delineate the specific conditions under which certain learning approaches outperform others. Furthermore, they provide an explanation as to why investors generally prefer committed businesses. We propose, however, that such commitment is not optimal in the long run and that, in general, firms should consider changing their learning approach over time in order to successfully renew their business model.     

不同彩色空间的彩色图像边缘检测研究

边缘信息是图像处理中的一种重要信息,通常的边缘检测研究是基于灰度图像的,然而彩色图像比灰度图像包含有更加丰富的边缘信息。彩色图像的边缘检测可以在不同的彩色空间中进行,目前大多数研究集中在RGB空间中。使用基于扩展的SUSAN边缘检测算子,将彩色图像在空间中的各分量分别进行边缘检测,对结果进行综合得到彩色图像的边缘检测结果,然后采用主观评价准则对若干不同的彩色空间中（包括RGB、YUV、YIQ、YCbCr、HSV、HSI）的试验结果进行了分析比较。试验结果表明,在HSV空间进行的边缘检测具有更好的性能。     

基于权因子调整的NURBS曲线形状修改算法及其在人脸建模中的应用

提出了一种修改NURBS曲线形状的新算法．该算法运用约束优化方法,通过调整原始曲线的多个控制顶点的权因子,使得修改后的曲线更加自然地通过给定的目标点,推导出了用于计算新权因子的3变量线性方程组,并在此基础上,针对人脸建模中的具体问题,设计了一种基于NURBS曲线形状调整的人脸轮廓线编辑算法,实验结果表明了该算法的有效性。     

基于不同色彩空间融合的快速图像增强算法

针对现有Retinex算法中存在的色彩失真、噪声放大及光晕伪影现象等问题,本文提出了一种基于Retinex理论的改进算法. 该算法首先在HSV空间对亮度分量V通道进行增强处理,同时在拉伸得到的对数域反射分量至一定的动态范围时（本文是0～255）,引入增强调整因子,调整不同亮度值的增强程度来避免噪声放大及色彩失真现象;然后在RGB空间,通过分析光晕产生的原因,提出一种改进的高斯滤波器来消除光晕现象,并在计算反射分量时,通过参数调整图像颜色的保真度. 最后,对上述两种不同颜色空间的处理结果进行加权平均作为算法的最终输出. 实验结果表明,针对不同光照条件下的图像,1）该算法可以明显地改善光晕伪影现象;2）无色彩失真、噪声放大等问题;3）效果和效率优于带色彩恢复的多尺度Retinex算法（Multi-scale retinex with color restoration,MSRCR）及其他对比算法.     

A Comparison between the Tikhonov and the Bayesian Approaches to Calculate Regularisation Matrices

Regularisation is a well-known technique for working with ill-posed and ill-conditioned problems that have been explored in a variety of different areas, including Bayesian inference, functional analysis, optimisation, numerical analysis and connectionist systems. In this paper we present the equivalence between the Bayesian approach to the regularisation theory and the Tikhonov regularisation into the function approximation theory framework, when radial basis functions networks are employed. This equivalence can be used to avoid expensive calculations when regularisation techniques are employed.