Piecewise Linear Time Series Estimation with GRASP

This paper describes a heuristic to build piecewise linear statistical models with multivariate thresholds, based on a Greedy Randomized Adaptive Search Procedure (GRASP). GRASP is an iterative randomized sampling technique that has been shown to quickly produce good quality solutions for a wide variety of optimization problems. In this paper we describe a GRASP to sequentially split an n-dimensional space in order to build a piecewise linear time series model.     

带季节线性趋势的季节时间序列的单位根检验

本文为检验带季节线性趋势的季节时间序列的单位根，提出了调整季节线性趋势的统计量，导出这些统计量的极限分布，并用Monte Carlo方法比较研究这些统计量的检验势．     

关于n维分段线性映射非光滑周期加倍分叉现象的一点注记

本文提供反例说明现有文献中关于n维分段线性映射非光滑周期加倍分叉现象的结论不成立,进而给出该结论的正确表述,并重新给予证明.     

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.     

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.     

基于主成分分析的单变量时间序列聚类方法

针对时间序列数据的高维特性,在进行理论分析的基础上,利用主成分分析法提出了一种单变量时间序列数据降维的新方法,进而提出了基于主成分分析的单变量时间序列聚类方法。其主要思想是在线性空间中的同一组基下,用系数之间的相似性来刻画对应时间序列之间相似性,在理论分析过程中,首先对单变量时间序列数据集进行主成分分析,其次分析了单变量时间序列数据集、样本协方差矩阵的特征向量与主成分之间的关系,并证明了由主成分构成的向量组线性无关。为了进一步验证理论分析结果的正确性和所提算法的有效性,分别利用仿真数据和真实的股票数据进行了数值实验。     

Web Quantlets for Time Series Analysis

New and advanced methods for nonlinear time series analysis are in general not available in standard software packages. Their implementation requires substantial time, computing power as well as programming skills. In time series analysis such a scenario is given by a recently suggested nonparametric lag selection procedure for univariate nonlinear autoregressive models which is based on the Corrected Asymptotic Final Prediction Error. In this paper we suggest a worldwide Web based specific client/server architecture that provides empirical researchers with fast access to new methods and powerful computing environments without knowing the statistical computing language and the server location. This architecture is implemented using the XploRe Quantlet technology and illustrated for nonparametric lag selection. Access to the Quantlet computing service can be obtained via standard WWW browsers or a Java client. The XploRe Quantlet service can be helpful in constructing research books and interactive teaching environments as the electronic version of this paper, available from http://ise.wiwi.hu-berlin.de/rolf/webquant.pdf, demonstrates.     

南通地区月降水量时间序列分析

根据南通地区1989年-2005年月降水量数据,在统计检验其平稳性、纯随机性的基础上,结合谱分析,建立该地区具有季节效应的疏系数ARIMA月降水量时间序列模型,对模型作了拟合预测检验.研究表明,多个模型的联合使用比单一模型更利于准确拟合预测.     

线性分式规划问题的多项式时间近似算法

本文针对线性比式和分式规划问题,提出一种求其全局最优解的完全多项式时间近似算法,并从理论上证明该算法的收敛性和计算复杂性,数值算例也说明了算法是可行的.     

关于时间序列分析的注记

本文讨论了时间序列分析中几个值得注意的问题 ;1自相关函数的意义 ,2趋势外推法预测中置信区间校正系数的计算 .     

专利申请与授权量的时间序列分析

专利的申请和授权量动态分析与预测是建立专利预警机制、设计与制定相关政策和战略的基础,具有重要的理论意义。起伏型时间序列法是一种新的时间序列分析法,提出用起伏型时间序列法对专利申请与授权数据进行动态分析。以2005年国内专利申请与授权数量月动态为研究数据,对专利申请、授权及发明专利申请数量的月动态进行建模模拟,结果令人满意,说明起伏型时间序列分析方法可应用于专利申请与授权动态模拟,从而丰富了专利申请与授权数据动态分析方法。     

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。     

基于动力学视角的经济时间序列分析

基于1978~2014年中国教育发展水平、就业人数与实际资本存量的数据,应用动力学原理建立模型,讨论了模型均衡解的稳定性,同时分析了该经济系统受到外部扰动时的动力学行为.研究结果表明,外部扰动产生的系统混沌效应会影响经济系统的正常运行,从而管理者有必要对经济系统提出有效的应对策略.因此,基于动力学原理,本文为时间序列动力学特征的微观刻画提供了一个视角.     

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.     

Generalized Rank Estimates For An Autoregressive Time Series: A U-Statistic Approach

A class of weighted rank-based estimates for estimating the parameter vector of an autoregressive time series is considered. This class of estimates is similar to, and contains, the class proposed by Terpstra et al. [54]. Asymptotic linearity properties are derived for the so called GR-estimates. Based on these properties, the GR-estimates are shown to be asymptotically normal at rate n ^1/2. The theory of U-statistics along with a characterization of weak dependence that is inherent in stationary AR(p) models are the primary tools used to obtain the results. The so called pair-wise slopes estimator, which is a special case of this class of estimates, is discussed in an AR(1) context. This revised version was published online in June 2006 with corrections to the Cover Date.     

A Geometric Approach to Global Optimization

In this paper we consider the problem of optimizing a piecewise-linear objective function over a non-convex domain. In particular we do not allow the solution to lie in the interior of a prespecified region R. We discuss the geometrical properties of this problems and present algorithms based on combinatorial arguments. In addition we show how we can construct quite complicated shaped sets R while maintaining the combinatorial properties.     

A Unified Monotonic Approach to Generalized Linear Fractional Programming

We present an efficient unified method for solving a wide class of generalized linear fractional programming problems. This class includes such problems as: optimizing (minimizing or maximizing) a pointwise maximum or pointwise minimum of a finite number of ratios of linear functions, optimizing a sum or product of such ratios, etc. – over a polytope. Our approach is based on the recently developed theory of monotonic optimization.     

Tabu Machine: A New Neural Network Solution Approach for Combinatorial Optimization Problems

A new artificial neural network solution approach is proposed to solve combinatorial optimization problems. The artificial neural network is called the Tabu Machine because it has the same structure as the Boltzmann Machine does but uses tabu search to govern its state transition mechanism. Similar to the Boltzmann Machine, the Tabu Machine consists of a set of binary state nodes connected with bidirectional arcs. Ruled by the transition mechanism, the nodes adjust their states in order to search for a global minimum energy state. Two combinatorial optimization problems, the maximum cut problem and the independent set problem, are used as examples to conduct a computational experiment. Without using overly sophisticated tabu search techniques, the Tabu Machine outperforms the Boltzmann Machine in terms of both solution quality and computation time.     

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.     

最终需求为时序变量的投入产出决策模型

针对最终需求为正态随机变量的投入产出模型,建立了一个时序模型来估计最终需求的期望与方差,并由此给出了总产出在一定置信度下的取值空间.这样就使得随机投入产出模型在决策时可具体操作.