A TIME SERIES APPROACH TO STOCK-RECRUITMENT ANALYSIS: TRANSFER FUNCTION NOISE MODELLING

Perceived relationships between stock size and recruitment have long been a corner-stone of fisheries management. These relationships are often used to design harvest strategies for ensuring that sufficient spawning stock exists to generate desired levels of recruitment in subsequent years. However, existing models fail to recognize and exploit the autocorrelation structure of both the recruitment and stock time series. The time series approach to modelling stock and recruitment presented in this paper takes this autocorrelation structure into account. The performance of the time series model is compared to existing stock-recruitment models using North Sea herring and Pacific halibut data.     

条件异方差双门限自回归模型的门限和延时的小波估计

本文讨论了条件异方差双门限自回归模型的门限和延时的识别问题,通过经验小波系数,给出了门限个数和门限以及延时的估计,在较弱的条例下,证明了所给的估计是相合的。     

ARMAX时间序列模型异常点及异常点斑片的估计和检测

将通常的Gibbs抽样和自适应的Gibbs抽样算法用于带有外生变量的自回归移动平均时间序列(ARMAX)模型的Bayes分析,首先采用一些方法消除ARMAX模型中输入(外生变量)序列的影响,然后在前人工作的基础上给出了一种类似的挖掘相应时间序列中的异常点及异常点斑片的方法.说明了自适应的Gibbs抽样算法也能够有效地检测ARMAX模型中孤立的附加型异常点及异常点斑片.实际的和模拟的结果也显示这些方法可以明显减少掩盖和淹没现象的发生,这是对已有工作的推广和扩充.     

一类不分明时间序列的回归预测

研究了一类不分明时间序列的线性回归预测问题,通过模糊数空间中的距离,建立了模糊环境中最小二乘回归模型,证明了回归模型解的存在性和唯一性,并给出了确定模型的模糊参数及检验模型拟合度的计算公式。     

基于时间序列法的国税月度收入预测模型研究

研究了基于时间序列方法的国税月度收入预测. 通过采用Box-Jenkins的ARIMA模型, 结合国税月度收入数据, 分析并提出了一套针对月度税收收入的预测研究框架, 包括对税收预测模型的拟合、检验、预测、评价、动态修正等主要环节的处理方法. 在该研究框架的指导下, 以增值税、海关代征税和营业税为例, 对2006年各月的税收收入进行了模拟预测, 月度税收收入预测的平均相对误差分别控制在5.47\%, 8.63\%和2.37\%. 最后给出了在实际应用中动态修正税收预测模型的建议, 并简要讨论了时间序列方法在税收预测中面临的问题.     

FIXED-DESIGN SEMIPARAMETRIC REGRESSION FOR LINEAR TIME SERIES

This article studies parametric component and nonparametric component estimators in a semiparametric regression model with linear time series errors; their r-th mean consistency and complete consistency are obtained under suitable conditions. Finally, the author shows that the usual weight functions based on nearest neighbor methods satisfy the designed assumptions imposed.     

FIXED-DESIGN REGRESSION FOR LINEARTIME SERIES

1 IntroductionLet A be a subset of tlie real line R. Consider a fixed design regression modelYni = g(Z.i) + Eni, 0 S i S n, (1.1)liere xnot', xnn E A, are design points, En1,' f Enn are randon errors f g is an unknownfnnctiou. Assunle that for each nl {E.i, 0 5 i S n} have the saxne distribution as {(i, 0 5 i S n},where (f is a general weakly stationary llnear processhere {Z,} is a nurtingale dtherence sequence ralative to an increasing sequence of J-fields Rcosuch that E(ZllR--,) …     

右删失数据下的时间序列数据分析

当平稳时间序列{Xn}被另外的平稳序列{Yn}删失后,我们研究{Xn}的协方差,相关系数的估计问题．特别当{Xn}是AR(p)序列时,我们研究AR(p)的参数估计和一步预测问题．给出的估计量是强相合的．计算机模拟结果说明所提供的方法是适用的．     

模糊时间数列的分析与预测:以台湾地区加权股价指数为例

随着我国经济快速成长，衍生性金融商品的投资分析，已成为国内财务数学研究热门课题。以股票市场而言，人们总希望比别人早一步掌握行情的脉动，以获取最高的报酬率，然而，影响股市加权股价指数波动的因素众多，要如何进行趋势分析与预测，是很多学者相当感兴趣与研究的主题。本文考虑以模糊统计方法，作模糊时间数列的趋势分析与预测。其望应用模糊统计分析方法比传统的时间数列分析方法能得到更合理的解释，且预测结果可以提供决策者更多的信息，做出正确的决策。最后以台湾地区加权股票指数为例，做一实证上的详细探讨。     

TESTING THE ADEQUACY OF GARCH-TYPE MODELS IN TIME SERIES

In this article a new approach for checking the adequacy of GARCH-type models in time series was proposed. The resulted tests involve weight functions, which provide them with the flexibility in choosing scores to enhance power performance. The choice of weight functions and the power properties of the tests are studied. For a large number of alternatives, asymptotically distribution-free maximin test is constructed. The tests are asymptotically chi-squared under the null hypothesis and easy to implement. Simulation results indicate that the tests perform well.     

Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization

In real time, one observation always relies on several observations. To improve the forecasting accuracy, all these observations can be incorporated in forecasting models. Therefore, in this study, we have intended to introduce a new Type-2 fuzzy time series model that can utilize more observations in forecasting. Later, this Type-2 model is enhanced by employing particle swarm optimization (PSO) technique. The main motive behind the utilization of the PSO with the Type-2 model is to adjust the lengths of intervals in the universe of discourse that are employed in forecasting, without increasing the number of intervals. The daily stock index price data set of SBI (State Bank of India) is used to evaluate the performance of the proposed model. The proposed model is also validated by forecasting the daily stock index price of Google. Our experimental results demonstrate the effectiveness and robustness of the proposed model in comparison with existing fuzzy time series models and conventional time series models.     

A STATE‐SPACE MODEL FOR UNIVARIATE ORDINAL‐VALUED TIME SERIES

Abstract A state‐space model was developed to analyze a univariate time series of ordinal‐valued flower phenology data. Flower abundance, recorded as either “none,”“some,” or “much,” was observed each month on trees in a lowland Costa Rican rain forest to investigate flowering patterns of different species. Data from a single Capparis pittieri and daily rain measurements are used to demonstrate the model. A method to obtain maximum likelihood estimates of the parameters and the use of predicted probability differences to assess goodness of fit are described. Opportunities for improving the model are also discussed.     

Modelling the impact of wind stress and river discharge on Danshuei River plume

A three-dimensional, time-dependent, baroclinic, hydrodynamic and salinity model, UnTRIM, was performed and applied to the Danshuei River estuarine system and adjacent coastal sea in northern Taiwan. The model forcing functions consist of tidal elevations along the open boundaries and freshwater inflows from the main stream and major tributaries in the Danshuei River estuarine system. The bottom friction coefficient was adjusted to achieve model calibration and verification in model simulations of barotropic and baroclinic flows. The turbulent diffusivities were ascertained through comparison of simulated salinity time series with observations. The model simulation results are in qualitative agreement with the available field data.     

多维平稳时间序列积分谱图的不变原理

用积分周期图估计平稳时间序列的谱函数，不论是高斯序列还是非高斯序列，其误差过程的不变原理都已被证明，许重光采用自迥归谱估计的积分估计谱函数也证明了误差过程的不变原理成立。本文讨论多维平稳时间序列，引进积分谱图来估计谱函数，证明了误差过程的不变原理在较弱的条件下成立。积分周期图是积分谱图的特例，因而［１］［３］［６］的一些结果是本文有关定理的特例。     

时序多指标决策的灰色关联分析法

本文对带有时间顺序的混合型多指标决策，运用灰色关联理论，建立了一种新的灰色关联决策模型，从而为时序多指标决策问题提供了又一科学，合理的决策方法。     

多维时间序列Granger因果性的一种图模型学习方法

传统的两变量Granger因果分析法容易产生伪因果关系,且不能刻画变量间的同期因果性.利用图模型方法研究多维时间序列变量间Granger因果关系,通过Granger因果图的建立将问题转化为Granger因果图结构的辨识问题,利用局部密度估计法构造相应的辨识统计量,采用bootstrap方法来确定检验统计量的原分布.模拟分析以及对于中国股市间Granger因果关系的研究说明了该方法的有效性.     

A nonlinear time series model and estimation of missing observations

This paper formulates a nonlinear time series model which encompasses several standard nonlinear models for time series as special cases. It also offers two methods for estimating missing observations, one using prediction and fixed point smoothing algorithms and the other using optimal estimating equation theory. Recursive estimation of missing observations in an autoregressive conditionally heteroscedastic (ARCH) model and the estimation of missing observations in a linear time series model are shown to be special cases. Construction of optimal estimates of missing observations using estimating equation theory is discussed and applied to some nonlinear models.Authors were supported in part by a grant from the Natural Sciences and Engineering Research Council of Canada.     

基于灰色系统的支持向量回归预测方法

根据部分时间序列数据贫信息、高噪声和非线性等特点,采用含边值修正的灰色模型进行预测,获取残差序列后运用支持向量回归（SVR）方法对模型进行残差修正得到复合的灰色支持向量回归模型．在支持向量回归中构造具有自适用性的动态惩罚参数G替代传统SVR中的不变参数来提高模型的准确性,同时构造算法决定￡以平滑过度调节．广东省工业生产指数的预测试验结果表明,复合模型具有比其他简单模型更理想的预测效果．     

基于人工神经网络和随机游走模型的汇率预测

由于金融数据具有随机性特征,使得建模和预测变得极其困难.提出一种组合预测方法,即假定任何金融时序数据由线性和非线性两部分组成,将其中线性部分的数据通过随机游走(RW)模型进行模拟,剩余的非线性残差部分由前馈神经网络(FANN)和诶尔曼神经网络(EANN)协同处理.从实证结果可知,该组合方法相比单独使用RW、FANN或EANN模型有更高的预测精度.