非线性时间序列建模的混合自回归滑动平均模型

提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA).该模型是由K个平稳或非平稳的ARMA分量经过混合得到的.讨论了MARMA模型的平稳性条件和自相关函数.给出了MARMA模型参数估计的期望极大化(expectation maximization)算法.运用贝叶斯信息准则(Bayes information criterion)来选择该模型.MARMA模型分布形式富于变化的特征使得它能够对具有多峰分布以及条件异方差的序列进行建模.通过两个实例验证了该模型,并和其他模型进行比较,结果表明MARMA模型能够更好地描述这些数据的特征.     

ARCH模型的研究与探讨

自回归条件异方差(ARCH)模型是近年来新发展起来的时间序列模型,它反映了随机 过程的一种特殊特性：即方差随时间变化而变化,且具有丛集性、波动性．ARCH模型已广泛 地应用于经济领域的建模及研究过程中．本文介绍了ARCH模型的特点,它的参数估计和检验 ,以及ARCH模型的发展情况．     

非线性时间序列建模的均值异方差混合转移分布模型

进一步研究了了由Berchtold提出的均值异方差混合转移分布(expectation heteroscedastic mixture transition distribution model,EHMTD)模型.讨论并得到了EHMTD模型的平稳性条什和分布函数的尾部特征.运用ECM(expectation conditional maximization)算法估计模型的参数.条件分布的多样性使得该类模型能够对非对称、多峰、厚尾等非Gauss特征进行描述.模拟及实例分析的结果表明EHMTD模型是一类易于建模,并且有着广泛应用前景的非线性时间序列模型.     

GARCH非线性时间序列模型的网络流量预测

网络流量预测在拥塞控制、网络管理与诊断、路由器设计等领域都具有重要意义。根据当今网络流量的特点,传统的ARMA模型在描述网络流量数据特性时有一定的局限性,从而影响网络流量预测的精度。针对这个问题,研究了使用广义自回归条件异方差模型(GARCH)对网络流量数据进行建模的方法,通过仿真实验表明,该模型可以较好地描述网络流量数据的异方差性,同时其预测精度较之传统的ARMA模型的预测精度也得到了大幅提升。     

基于组合插值的GM(1,1)模型背景值的改进*

针对 GM(1,1) 模型预测误差偏大的问题,对GM(1,1)模型背景值的构造形式进行了研究。为了能够更加有效地降低GM(1,1)模型的预测误差,提出了基于辛普森3/8公式和牛顿插值公式的组合插值方法来构造出新的GM(1,1)模型的背景值。在GM(1,1)模型的建模过程中,由于原始建模数据序列中的第一个数据没有参与建模, 导致原始数据序列的数据资源利用效率降低,影响了GM(1,1)模型预测精度。因此,可以通过把灰色协调系数b加在原始建模数据序列前面的方法,使第一个数据能够参与到GM(1,1)模型的建模过程中。为了检验模型的改进效果,进行了原始建模数据类型分别为纯指数型数据序列、稳定型数据序列和缺失型数据序列的三组实验。对每组测试实验的预测结果进行对比分析,可以发现,基于组合插值方法对GM(1,1)模型的背景值进行改进,可以极大地降低GM(1,1)模型的模拟和预测误差。改进后的模型具有比较好的预测稳定性,增强了GM(1,1)模型的适用性。     

一种基于判别随机场模型的联机行为识别方法

提出了一种基于判别随机场模型的联机行为识别方法,将传统的随机场模型和隐藏条件随机场模型的特点相结合,构建一个针对于运动序列帧数据建模的帧-隐藏条件随机场模型,并将该模型应用于数据驱动的行为建模,利用传统条件随机场模型对行为间的运动特性进行建模;通过引入隐藏特征函数,设计有效的特征模板来表示行为中子姿态的联系,实现对行为的内在运动特性进行建模.通过对比实验表明,该模型对于联机处理行为序列具有更强的识别能力.     

一类非线性周期时间序列模型

为了描述周期时间序列中的偏倚和多峰等非线性特征,结合有限混合模型方法,提出混合周期自回归滑动平均时间序列模型（MPARMA）,给出了MPARMA模型的平稳性条件,讨论了期望最大化（EM）算法的应用,通过PM10浓度序列分析,评估了MPARMA模型的表现。     

时间序列分析方法及ARMA，GARCH两种常用模型

证券市场具有数据单一性（大量不需要经过特殊处理的数据）、分析手段多样性和隐蔽性的特点,且与其飞速发展不相称的是证券分析技术进展的缓慢。股市系统中时间序列的预测问题具有重要的理论及实际意义,时间序列的获取是通过对数据库中数据进行分类汇总分析而获得。获取时间序列数据以后可以对它进行预测分析,从而较准确地预见系统的演进。文中介绍了时间序列的基本知识,同时比较了ARMA和GARCH两种常用模型,得出对于中国股市,GARCH模型性能优于ARCH模型。     

时间序列分析方法及ARMA,GARCH两种常用模型

证券市场具有数据单一性（大量不需要经过特殊处理的数据）、分析手段多样性和隐蔽性的特点,且与其飞速发展不相称的是证券分析技术进展的缓慢。股市系统中时间序列的预测问题具有重要的理论及实际意义,时间序列的获取是通过对数据库中数据进行分类汇总分析而获得。获取时间序列数据以后可以对它进行预测分析,从而较准确地预见系统的演进。文中介绍了时间序列的基本知识,同时比较了ARMA和GARCH两种常用模型,得出对于中国股市,GARCH模型性能优于ARCH模型。     

基于高阶灰色系统模型对水产品产量数据的实证分析

基于高阶灰色系统模型理论对具有指数型增长且有微小扰动的时间序列进行建模的良好拟合特性,文章依次用GM(1,1),GM(2,1),GM(3,1)模型对中国历年水产品产量时间序列进行了实证分析。在对我国水产品产量数据的拟合过程中,因该数据具有指数型增长和随机性的特征,高阶灰色模型GM(2,1)较比普通的模型GM(1,1)有更好的拟合效果;同时因数据变化具有显著的阶段性,遂GM(3,1)的建模结果很不理想,不具有实际意义。文章对我国水产品产量数据所作的数学分析对有关部门进行水产品政策的研究与制定有借鉴作用。     

Modeling Hong Kong’s stock index with the Student t-mixture autoregressive model

It is well known that financial returns are usually not normally distributed, but rather exhibit excess kurtosis. This implies that there is greater probability mass at the tails of the marginal or conditional distribution. Mixture-type time series models are potentially useful for modeling financial returns. However, most of these models make the assumption that the return series in each component is conditionally Gaussian, which may result in underestimates of the occurrence of extreme financial events, such as market crashes. In this paper, we apply the class of Student t-mixture autoregressive (TMAR) models to the return series of the Hong Kong Hang Seng Index. A TMAR model consists of a mixture of g autoregressive components with Student t-error distributions. Several interesting properties make the TMAR process a promising candidate for financial time series modeling. These models are able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time-varied from short- to long-tailed or from unimodal to multi-modal. The use of Student t-distributed errors in each component of the model allows for conditional leptokurtic distribution, which can account for the commonly observed unconditional kurtosis in financial data.     

Image Fusion with Conditional Probability Networks for Monitoring the Salinization of Farmland

We show how a series of satellite images can be used in conjunction with data derived from a digital terrain model to monitor salinity in farmland. A conditional probability network (CPN) is constructed to produce salinity maps by combining uncertain information in images with uncertain knowledge or rules, where the rules are of a temporal and spatial nature. A specific model for extending conditional probability networks to handle the case of spatial context is given. To implement this model requires minor modifications to existing code for handling nonspatial CPN's.     

Diversity of quantum optimizations for training adaptive support vector regression and its prediction applications

Three kinds of quantum optimizations are introduced in this paper as follows: quantum minimization (QM), neuromorphic quantum-based optimization (NQO), and logarithmic search with quantum existence testing (LSQET). In order to compare their optimization ability for training adaptive support vector regression, the performance evaluation is accomplished in the basis of forecasting the complex time series through two real world experiments. The model used for this complex time series prediction comprises both BPNN-Weighted Grey-C3LSP (BWGC) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) that is tuned perfectly by quantum-optimized adaptive support vector regression. Finally, according to the predictive accuracy of time series forecast and the cost of the computational complexity, the concluding remark will be made to illustrate and discuss these quantum optimizations.     

Modeling threshold conditional heteroscedasticity with regime-dependent skewness and kurtosis

Construction of nonlinear time series models with a flexible probabilistic structure is an important challenge for statisticians. Applications of such a time series model include ecology, economics and finance. In this paper we consider a threshold model for all the first four conditional moments of a time series. The nonlinear structure in the conditional mean is specified by a threshold autoregression and that of the conditional variance by a threshold generalized autoregressive conditional heteroscedastic (GARCH) model. There are many options for the conditional innovation density in the modeling of the skewness and kurtosis such as the Gram-Charlier (GC) density and the skewed-t density. The Gram-Charlier (GC) density allows explicit modeling of the skewness and kurtosis parameters and therefore is the main focus of this paper. However, its performance is compared with that of Hansen’s skewed-t distribution in the data analysis section of the paper. The regime-dependent feature for the first four conditional moments allows more flexibility in modeling and provides better insights into the structure of a time series. A Lagrange multiplier (LM) test is developed for testing for the presence of threshold structure. The test statistic is similar to the classical tests for the presence of a threshold structure but allowing for a more general regime-dependent structure. The new model and the LM test are illustrated using the Dow Jones Industrial Average, the Hong Kong Hang Seng Index and the Yen/US exchange rate.     

网络日志数据中条件因果挖掘算法的优化研究

网络操作中收集了大量的系统日志数据,找出精确的系统故障成为重要的研究方向.提出一种条件因果挖掘算法(CCMA),通过从日志消息中生成一组时间序列数据,分别用傅里叶分析和线性回归分析删除大量无关的周期性时间序列后,利用因果推理算法输出有向无环图,通过检测无环图的边缘分布,消除冗余关系得出最终结果.仿真结果表明,对比依赖挖掘算法(DMA)和网络信息关联与探索算法(NICE),CCMA算法在处理时间和边缘相关率2个主要性能指标方面均有改善,表明CCMA算法在日志事件挖掘中能有效优化处理速度和挖掘精度.     

基于增强约束条件随机场的Web对象信息抽取

线性链条件随机场模型难以处理Web对象与各个标注属性之间的特征关系,为解决此问题,提出一种增强约束条件随机场模型。通过将约束条件引入推理过程,改进线性链条件随机场模型的Viterbi算法;运用最大间隔理论的思想训练条件随机场模型,提高模型标注的正确率;将该模型与条件随机场模型及层次条件随机场模型进行对比。实验结果表明该模型能在提高标注正确率的基础上有效地解决Web对象信息抽取问题。     

Foundations of compositional models: structural properties

The paper is a follow-up of [R.J.: Foundations of compositional model theory. IJGS, 40(2011): 623–678], where basic properties of compositional models, as one of the approaches to multidimensional probability distributions representation and processing, were introduced. In fact, it is an algebraic alternative to graphical models, which does not use graphs to represent conditional independence statements. Here, these statements are encoded in a sequence of distributions to which an operator of composition – the key element of this theory – is applied in order to assemble a multidimensional model from its low-dimensional parts. In this paper, we show a way to read conditional independence relations, and to solve related topics, above all the so-called equivalence problem, i.e. the problem of recognizing whether two different structures induce the same system of conditional independence relations.