多元时间序列模型协方差矩阵的递归算法

本推导了多元时序横型的协方差矩阵与模型参数的关系式，并给出了计算多维时序过程自协方差矩阵的递归算法。     

关于ARMA序列协方差矩阵的逆

本文用矩阵方法导出ＡＲＭＡ（ｐ，ｑ）序列协方差阵的逆的一种表达式，由它可以较快计算平方和函数及其偏导数，还可以求得初值为零的条件平方和函数的误差。     

自协方差非平稳时间序列的时变参数自回归模型

对一簇时间序列明确定义了自协方差非平稳时间序列.对于自协方差非平稳时间序列,提出了用于自协方差非平稳时间序列的3种时变参数自回归（TVPAR）模型：满阶TVPAR模型、非时变阶次TVPAR模型和时变阶次TVPAR模型.并进行了有关的最小赤池信息量准则（AIC）估计.     

多元线性递归序列的Hopf代数结构

本文推广了Peterson和Taft的主要结果.主要证明了F[x₁,x₂,… ,x_n]⁰与所有n元线性递归序列组成的集合是1-1对应的.从而可赋予后者一个Hopf代数结构.这样对研究多元线性递归序列内的运算性质(如Hurwitz与Hadamard乘法)提供了方便.此外还进一步研究了线性递归序列的解空间理论.并证明了n元线性递归序列的解空间由一个线性齐次偏微分方程组唯一确定.     

基于结构矩阵的DNA序列的相似性模型

通过一维映射把DNA序列转化为时间序列,即把数字1,2,3,4分别分配给组成DNA序列的核苷酸A,T,G,C,用时间序列的结构矩阵来描述DNA序列的结构特征,并根据结构矩阵的一些性质定义了结构矩阵的相似性度量,进而利用结构矩阵之间的相似性度量构建了比较DNA序列的相似性模型,以9个不同物种的β-球蛋白基因的第一个外显子(表1)为例验证了该模型的适用性.并得到了较好得结果.     

多元协方差分析用于艾滋病疗法的选择

目的:优化HAART药物治疗方案,提高治疗效果,为广大患者和医生选择艾滋病的疗法提供科学的依据.方法:以CD4细胞数的增加值作为体现治疗效果的因变量,治疗时间和初始治疗CD4细胞数为协变量,通过四组疗法分组进行多元协方差分析。结果:患者治疗效果的差异主要是由于治疗方法和治疗时间以及初始治疗cd4值的差异所致;四种疗法的疗效总体上差异显著,满足完备性条件和显著性条件的疗法疗效优劣顺序是:M4>M2≈M3≈M1.结论:多元协方差分析用于艾滋病疗法的选择对实际工作有积极的指导意义.     

基于单个观测值用于检测多元协方差矩阵的EWMA控制图

基于单个样本观测值提供了一个指数加权移动平均(EWMA)控制图用于监测p维多元正态过程协方差矩阵的飘移。模拟研究表明,此图对各种形式的飘移都有很好的表现,且不受均值变化的影响。实例表明,新设计的控制图在实际应用中具有很好的表现。     

对角双线性时间序列的近似三阶矩结构和双谱密度

三阶矩结构和双谱是分析和研究非线性时间序列的重要方法。但对于双线性时间序列来说，计算两者的精确值是困难的。因此，寻找比较好的近似值就非常有必要。本文针对一般的对角双线性时间序列，给出了计算近似三阶矩和双谱密度的公式。仿真计算表明，这些公式是有效的。     

时间序列价格模型及其应用

1 预备知识 设A=（aij）m×m表示m个行业的投入产出消耗系数矩阵;P（0）=（p1（0）,p2（0）,…pm（0））是基年的初始价格向量;pj（0）（j=1,2,…,m）是基年的第j个行业的产出价格,P（n-1）=     

上次对角双线性时间序列模型的近似三阶矩结构和双谱密度

本文讨论一类特殊的双线性时间序列模型，该模型中，每一个双线性项均为两个相关的随机变量的乘积，该模型的二阶结构(自协方差和谱)目前已经很清楚了，同线性ARMA模型类似，亦具有有理谱密度，理论上，为了识别该模型，通常是考虑三阶结构(三阶矩和双谱)，但由于该模型的复杂性，精确的三阶结构是无法求出的，本文给出一种计算近似三阶矩的公式，由此得到了近似的双谱密度，仿真计算验证了这种方法是有效的。     

Autocovariance Function Estimation via Penalized Regression

The work revisits the autocovariance function estimation, a fundamental problem in statistical inference for time series. We convert the function estimation problem into constrained penalized regression with a generalized penalty that provides us with flexible and accurate estimation, and study the asymptotic properties of the proposed estimator. In case of a nonzero mean time series, we apply a penalized regression technique to a differenced time series, which does not require a separate detrending procedure. In penalized regression, selection of tuning parameters is critical and we propose four different data-driven criteria to determine them. A simulation study shows effectiveness of the tuning parameter selection and that the proposed approach is superior to three existing methods. We also briefly discuss the extension of the proposed approach to interval-valued time series. Supplementary materials for this article are available online.     

Enabling Interactivity on Displays of Multivariate Time Series and Longitudinal Data

Temporal data are information measured in the context of time. This contextual structure provides components that need to be explored to understand the data and that can form the basis of interactions applied to the plots. In multivariate time series, we expect to see temporal dependence, long term and seasonal trends, and cross-correlations. In longitudinal data, we also expect within and between subject dependence. Time series and longitudinal data, although analyzed differently, are often plotted using similar displays. We provide a taxonomy of interactions on plots that can enable exploring temporal components of these data types, and describe how to build these interactions using data transformations. Because temporal data are often accompanied other types of data we also describe how to link the temporal plots with other displays of data. The ideas are conceptualized into a data pipeline for temporal data and implemented into the R package cranvas. This package provides many different types of interactive graphics that can be used together to explore data or diagnose a model fit.     

Variational Bayes Estimation of Discrete-Margined Copula Models With Application to Time Series

We propose a new variational Bayes (VB) estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior and is faster than previous likelihood-based approaches. We use it to estimate drawable vine copulas for univariate and multivariate Markov ordinal and mixed time series. These have dimension rT, where T is the number of observations and r is the number of series, and are difficult to estimate using previous methods. The vine pair-copulas are carefully selected to allow for heteroscedasticity, which is a feature of most ordinal time series data. When combined with flexible margins, the resulting time series models also allow for other common features of ordinal data, such as zero inflation, multiple modes, and under or overdispersion. Using six example series, we illustrate both the flexibility of the time series copula models and the efficacy of the VB estimator for copulas of up to 792 dimensions and 60 parameters. This far exceeds the size and complexity of copula models for discrete data that can be estimated using previous methods. An online appendix and MATLAB code implementing the method are available as supplementary materials.     

A Recursive Algorithm for Adaptive Estimation and Parameter Change Detection of Time Series Models

The discounted recursive least-squares concept of R. G. Brown's adaptive smoothing is extended to the Box-Jenkins models. The D.R.L.S. algorithm is derived for the autoregressive moving-average models. Two important applications of the D.R.L.S. for parameter change detection and adaptive forecasting are presented.     

Evolving Time Series Forecasting ARMA Models

Time Series Forecasting (TSF) allows the modeling of complex systems as black-boxes, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.     

周期相关时间序列与周期自回归模型

介绍了周期相关时间序列和周期自回归模型,并研究了周期自回归时间序列的稳定性及周期性,得到了它为周期相关时间序列的一个充要条件,推广了文献[1]的结论.     

股票指数的时间序列模型分析

借助于SA S软件将工程中的K a lm an滤波方法与时间序列的状态空间模型结合对上海A股指数进行了拟合与预测分析,通过对拟合与预测误差的计算可以发现这种模型是可行的;然后还把与滤波结合的状态空间模型的分析结果和常见的时间序列模型如:AR IM A模型、逐步自回归模型以及指数平滑模型的分析结果进行比较,比较的结果说明结合滤波的状态空间模型分析的结果比后三种的结果更加精确.结果为时间序列数据分析提供了一个较好的分析工具.     

Estimating Functions for Nonlinear Time Series Models

This paper discusses the problem of estimation for two classes of nonlinear models, namely random coefficient autoregressive (RCA) and autoregressive conditional heteroskedasticity (ARCH) models. For the RCA model, first assuming that the nuisance parameters are known we construct an estimator for parameters of interest based on Godambe's asymptotically optimal estimating function. Then, using the conditional least squares (CLS) estimator given by Tjøstheim (1986, Stochastic Process. Appl., 21, 251–273) and classical moment estimators for the nuisance parameters, we propose an estimated version of this estimator. These results are extended to the case of vector parameter. Next, we turn to discuss the problem of estimating the ARCH model with unknown parameter vector. We construct an estimator for parameters of interest based on Godambe's optimal estimator allowing that a part of the estimator depends on unknown parameters. Then, substituting the CLS estimators for the unknown parameters, the estimated version is proposed. Comparisons between the CLS and estimated optimal estimator of the RCA model and between the CLS and estimated version of the ARCH model are given via simulation studies.     

基于修正的时间序列模型对冲击负荷的预测

电力负荷预测的实质是对电力市场需求的预测,是利用以往的历史数据资料找出电力负荷的变化规律,进而预测负荷在未来时期的变化趋势.由于经济、气候以及工业生产等诸多因素的约束和限制,电力负荷预测精度很难提高.一个好的实用的电力负荷预测模型则要求既能充分利用负荷的历史数据,又能灵活方便地综合考虑其他多种相关因素的影响.提出了回归与自回归模型相结合的时间序列混合回归预测模型,它的待估参数由BP神经网络进行修正,经实例验证,预测效果良好.     

一类非线性时序模型非遍历性的一个充分条件

本文研究了一类由紧算子与加性i.i.d.干扰确定的非线性时间序列的非遍历性,揭示了这类非线性时间序列的非遍历性与其相应确定性部分的Lyapunov函数之间的联系。