Autoregression Model of Time Series with Matrix Cross-Section Data北大核心CSCD

矩阵型截面数据时间序列的优点在于可以同时刻画多个对象的多个属性.本文重点研究了矩阵型截面数据时间序列的自回归模型,给出了该模型的参数估计、模型识别、白噪声检验三个方面的理论结果.最后再利用矩阵型截面数据时间序列自回归模型,对两支银行股的日收益率序列和日成交量变化率序列进行建模分析.     

不等间隔动态数据的差分建模

本文给出了不等间隔动态数据的差分建模方法 ,分别对单调型和起伏型动态序列建模预测进行了探讨 ,文章最后对苏州虎丘塔倾斜形变进行了预测 .     

基于三角模糊数序列的灰色预测模型

以GM(1,1)模型为代表的灰色预测模型是以精确数序列为基础,难以满足实际需要.为了使灰色模型适应于模糊数序列,具体给出了一种基于三角模糊数序列的建模方法,这种方法也可以实现对二元区间模糊数和梯形模糊数序列的建模.首先由三角模糊数序列得出三个含有等量信息的精确数序列:重心序列、隶属函数的覆盖面积序列和中界点序列,对这三个序列分别建模后,再导出原始三角模糊数序列的三个界点的预测模型.这种建模方法既保持了模糊数的整体性又提高了建模序列的光滑度,提高了预测精度.最后进行了多组随机三角模糊数序列的数据模拟,验证了模型的有效性.     

基于BOX-Jenkins方法的青南高原降水量时间序列分析建模与预测

应用基于Box-Jenkins方法的时间序列分析技术,对青南高原的四个典型地区1961-2005年降水量序列进行ARMA建模分析:验证了四地区年降水量序列的时间序列特性,研究并选择了这些序列的最佳ARMA模型,本文也通过模型对未来降水量进行了预测.模型实证分析的结果表明:在青藏高原降水量时间序列分析建模与预测方面,Box-Jenkins方法及其模型是一种精度较高且切实有效的方法模型.     

基于Riccati方程的灰色多变量时滞MDRGM(1,N,α)预测模型

针对Riccati方程中含有多变量时滞效应的灰色系统建模问题,提出一种多变量时滞MDRGM(1,N,α)预测模型及其求解方法,给出了参数估计计算过程及近似响应式.在此基础上,通过数据仿真及不同模型间的对比分析验证了模型的有效性、准确性.结果表明:新构建的预测模型在预测具“S”型特征的时间序列数据时具有一定的应用价值.     

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。     

ARCH模型分布变点监测与应用

分布变点监测是时间序列交点分析的一个重要内容.为将分布交点监测从线性时间序列模型拓展到非线性时间序列模型,提出一种经验特征函数型的统计量监测ARCH模型误差项平方的分布变点,给出了监测统计量在原假设下的极限分布,并证明了此方法的一致性,用Bootstrap重抽样方法获得了极限分布的临界值,并和Kolmogorov-smirnov型监测统计量进行了比较.模拟结果和实例分析说明了当已观测样本量较大时,采用经验特征函数型统计量监测效果较好.     

太阳辐射时间序列的非线性检验

检验太阳辐射时间序列是否有非线性特征,对于分析、建模和预测太阳辐射量是重要、有益的.提出用基于替代数据的检验方法来检验太阳辐射时间序列是否存在非线性特征,并将数据序列的三阶矩作为检验统计量.选取了美国Montana州Dillon地区和Wyoming州Green Rivet地区每日总辐射量、Utah州Moab地区的每月日平均总辐射量时间序列作为检验对象.数值分析的统计结果表明所研究的日总辐射时间序列存在非线性,而每月日平均总辐射时间序列未检测出非线性.因而,对太阳辐射时间序列建模和预测之前,检验其是否有非线性特征是必要的.     

基于ARIMA和LSSVM的非线性集成预测模型

针对复杂时间序列预测困难的问题,在综合考虑线性与非线性复合特征的基础上,提出一种基于ARIMA和最小二乘支持向量机(LSSVM)的非线性集成预测方法.首先采用ARIMA模型进行时间序列线性趋势建模,并为LSSVM建模确定输入阶数;接着根据确定的输入阶数进行时间序列样本重构,采用LSSVM模型进行时间序列非线性特征建模;最后采用基于LSSVM的非线性集成技术形成一个综合的预测结果.将该方法用于中国GDP预测取得的结果,与单独预测方法及流行的其他集成预测方法相比,预测精度有了较大的提高,从而验证了方法的有效性和可行性.     

基于BP神经网络的时间序列预测问题研究

分析指出了基于标准BP神经网络的时间序列预测问题存在的不足.根据基于BP神经网络的时间序列预测问题的特点,研究给出了一种以y=x作为传递函数的时间序列预测方法,经实例验证表明,给出的以y=x作为传递函数的时间序列预测方法较基于标准BP神经网络的时间序列预测方法具有较好的结果.     

Using interval information granules to improve forecasting in fuzzy time series

In the process of modeling and forecasting of fuzzy time series, an issue on how to partition the universe of discourse impacts the quality of the forecasting performance of the constructed fuzzy time series model. In this paper, a novel method of partitioning the universe of discourse of time series based on interval information granules is proposed for improving forecasting accuracy of model. In the method, the universe of discourse of time series is first pre-divided into some intervals according to the predefined number of intervals to be partitioned, and then information granules are constructed in the amplitude-change space on the basis of data of time series belonging to each of intervals and their corresponding change (trends). In the sequel, optimal intervals are formed by continually adjusting width of these intervals to make information granules which associate with the corresponding intervals become most “informative”. Three benchmark time series are used to perform experiments to validate the feasibility and effectiveness of proposed method. The experimental results clearly show that the proposed method produces more reasonable intervals exhibiting sound semantics. When using the proposed partitioning method to determine intervals for modeling of fuzzy time series, forecasting accuracy of the constructed model are prominently enhanced.     

灰色预测法的进一步推广及应用

把原始时间序列进行多次累加,产生多个新序列,研究它们之间的关系,建立多元线性(或非线性)回归模型.给定显著性水平α,对回归方程进行显著性检验,在置信度1-α下,利用微分,差分关系建立相应的高阶微分方程,从而实现对原序列的预测或控制.该方法进一步推广了灰色预测法,为时间序列建模提供了一个新手段,一些著名的微分方程模型成为这一方法的特例,最后,用该方法对中国1979年至2008年的GDP序列建立微分方程模型并进行预测.     

State-space modeling for seismic signal analysis

The time series utilized for geodetic signal analysis, such as strain and groundwater level data, usually is largely affected by barometric pressure, earth tide and precipitation, and also suffer from missing observations due to instrument maintenance or breakdown. To detect informative geodetic signal from heavily noise-affected data, one must build a time series model for decomposition of the data taking into account the characteristics of effects from these covariates. This paper proposes a new modeling method for detecting geodetic signal from earthquake-related time series data by introducing pole-restricted precipitation model, jump component and pre-processing with AR model for interpolating missing observations. Using the proposed method, a geodetic sample data can be decomposed stably into several components including geodetic trend signal, barometric pressure response, earth tidal response, precipitation response and data level shift due to mechanical maintenance or breakdown. The decomposition of the time series and the interpolation of the missing observations are performed very efficiently by using the state-space representation and the Kalman filter/smoother. Finally, case studies of real geodetic sample data demonstrate the effectiveness of the proposed modeling method that lead to some important findings in seismology.     

有序判别分析新算法及其应用

判别分析是用已知分类数据建模对未知分类数据进行判别的方法,所用数据和分类不分顺序。要对有序又有周期数据进行判别分析,就要探索有序判别的新方法。这种方法的分类应当是有序的,并且能够排除事物发展周期性的干扰。本文介绍多元数据有序判别分析新方法的原理、建模流程、应用流程和应用实例。这种判别分析将分类建模与判别归类分开。新方法对多元数据建模时在多类模型中建立滑移的多套子模型,应用时根据应用领域的知识对样本归属作初步预估,然后程序选择相关的子模型进行判别归类。这种方法解决了由于时间序列多元数据周期性造成的样本分类颠倒问题,为时间序列数据的分类和预测开辟了新途径,在实际应用中取得了良好的效果,解决了重大难题。     

动态数据建模方法新探

本文利用微分方程的数值解法对时间序列建模预测作了新的尝试。文中用实例给以说明。     

Linear combination rule in genetic algorithm for optimization of finite impulse response neural network to predict natural chaotic time series

A finite impulse response neural network, with tap delay lines after each neuron in hidden layer, is used. Genetic algorithm with arithmetic decimal crossover and Roulette selection with normal probability mutation method with linear combination rule is used for optimization of FIR neural network. The method is applied for prediction of several important and benchmarks chaotic time series such as: geomagnetic activity index natural time series and famous Mackey–Glass time series. The results of simulations shows that applying dynamic neural models for modeling of highly nonlinear chaotic systems is more satisfactory with respect to feed forward neural networks. Likewise, global optimization method such as genetic algorithm is more efficient in comparison of nonlinear gradient based optimization methods like momentum term, conjugate gradient.     

Measuring the baseline sales and the promotion effect for incense products: a Bayesian state-space modeling approach

One of the most important research fields in marketing science is the analysis of time series data. This article develops a new method for modeling multivariate time series. The proposed method enables us to measure simultaneously the effectiveness of marketing activities, the baseline sales, and the effects of controllable/uncontrollable business factors. The critical issue in the model construction process is the method for evaluating the usefulness of the predictive models. This problem is investigated from a statistical point of view, and use of the Bayesian predictive information criterion is considered. The proposed method is applied to sales data regarding incense products. The method successfully extracted useful information that may enable managers to plan their marketing strategies more effectively.     

Quantile forecasting and data-driven inventory management under nonstationary demand

In this paper, a single-step framework for predicting quantiles of time series is presented. Subsequently, we propose that this technique can be adopted as a data-driven approach to determine stock levels in the environment of newsvendor problem and its multi-period extension. Theoretical and empirical findings suggest that our method is effective at modeling both weakly stationary and some nonstationary time series. On both simulated and real-world datasets, the proposed approach outperforms existing statistical methods and yields good newsvendor solutions.     

The two-filter formula for smoothing and an implementation of the Gaussian-sum smoother

A Gaussian-sum smoother is developed based on the two filter formula for smoothing. This facilitates the application of non-Gaussian state space modeling to diverse problems in time series analysis. It is especially useful when a higher order state vector is required and the application of the non-Gaussian smoother based on direct numerical computation is impractical. In particular, applications to the non-Gaussian seasonal adjustment of economic time series and to the modeling of seasonal time series with several outliers are shown.