共查询到20条相似文献,搜索用时 31 毫秒
1.
AR and bilinear time series models are expressed as time series chain graphical models, based on which, it is shown that the
coefficients of AR and bilinear models are the conditional correlation coefficients conditioned on the other components of
the time series. Then a graphically based procedure is proposed to test the significance of the coefficients of AR and bilinear
time series. Simulations show that our procedure performs well both in sizes and powers.
This work was supported by the Hong Kong Polytechnic University Research Council, the National Natural Science Foundation
of China (Grant No. 10671044) and the Science and Technology Bureau of Guangzhou Municipal Government of China (Grant No.
LSBH-017) 相似文献
2.
The purposes of this paper are to introduce a multivariate non-stationary stochastic time series model without individual detrending and to extract the multiple relationships between variables. To infer the statistical relation between variables, we attempt to estimate the co-movement of multivariate non-stationary time series components. The model is expressed in state-space form, and time series components are estimated by the maximum likelihood method using numerical optimization algorithm. The Kalman filter algorithm is used to compute the likelihood of the model. The AIC procedure gives a criterion for selecting the best model fit for the data. The multiple relationship becomes clear by analysing estimated AR coefficients. Real economic data are used for a numerical example. 相似文献
3.
Richard A. Davis Pengfei Zang Tian Zheng 《Journal of computational and graphical statistics》2016,25(4):1077-1096
The vector autoregressive (VAR) model has been widely used for modeling temporal dependence in a multivariate time series. For large (and even moderate) dimensions, the number of the AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions, and difficult-to-interpret temporal dependence. To overcome such drawbacks, we propose a two-stage approach for fitting sparse VAR (sVAR) models in which many of the AR coefficients are zero. The first stage selects nonzero AR coefficients based on an estimate of the partial spectral coherence (PSC) together with the use of BIC. The PSC is useful for quantifying the conditional relationship between marginal series in a multivariate process. A refinement second stage is then applied to further reduce the number of parameters. The performance of this two-stage approach is illustrated with simulation and real data examples. Supplementary materials for this article are available online. 相似文献
4.
Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models: Logistic, AR(1) and AR(2). The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR(1) model and the AR(2) model of zero-mean series of the weekly closing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price. Six common statistical methods for error prediction are used to test the predicting results. These methods are: mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), Akaike's information criterion (AIC), and Bayesian information criterion (BIC). The investigation shows that AR(1) model exhibits the best predicting result, whereas AR(2) model exhibits predicting results that is intermediate between AR(1) model and the Logistic regression model. 相似文献
5.
1. IntroductionDetection of jump points often arises in many practical problems such as signal analysis,.... fimage processing, seismic exploratioll and phonetic identification. FOr examPle, financialeconollilsts often wad to know if abrupt changes occur in an exchange rate series sincethese changes edicted, are affecting or will affect fin-ancial market; engineers concern abolltwhether there exist jumps in a seismic signal in oil exploration bacause these jumps maypredict that there exists br… 相似文献
6.
For about thirty years, time series models with time-dependent coefficients have sometimes been considered as an alternative
to models with constant coefficients or non-linear models. Analysis based on models with time-dependent models has long suffered
from the absence of an asymptotic theory except in very special cases. The purpose of this paper is to provide such a theory
without using a locally stationary spectral representation and time rescaling. We consider autoregressive-moving average (ARMA)
models with time-dependent coefficients and a heteroscedastic innovation process. The coefficients and the innovation variance
are deterministic functions of time which depend on a finite number of parameters. These parameters are estimated by maximising
the Gaussian likelihood function. Deriving conditions for consistency and asymptotic normality and obtaining the asymptotic
covariance matrix are done using some assumptions on the functions of time in order to attenuate non-stationarity, mild assumptions
for the distribution of the innovations, and also a kind of mixing condition. Theorems from the theory of martingales and
mixtingales are used. Some simulation results are given and both theoretical and practical examples are treated.
Received 2004; Final version 23 December 2004 相似文献
7.
We propose a minimum mean absolute error linear interpolator (MMAELI), based on theL
1 approach. A linear functional of the observed time series due to non-normal innovations is derived. The solution equation
for the coefficients of this linear functional is established in terms of the innovation series. It is found that information
implied in the innovation series is useful for the interpolation of missing values. The MMAELIs of the AR(1) model with innovations
following mixed normal andt distributions are studied in detail. The MMAELI also approximates the minimum mean squared error linear interpolator (MMSELI)
well in mean squared error but outperforms the MMSELI in mean absolute error. An application to a real series is presented.
Extensions to the general ARMA model and other time series models are discussed.
This research was supported by a CityU Research Grant and Natural Science Foundation of China. 相似文献
8.
P. B. Reddy 《Applied Mathematical Modelling》1977,1(7):367-371
The autocorrelation function of seasonal time series data is shown to have peaks which occur at the correlation lags equal to the integer multiples of the fundamental period that is present in the series. This property is shown to be valid even if some of the harmonics including the fundamental are removed from the time series data. Using this property, an analytical procedure is presented for estimating the variance of the white noise generating the low frequency random walk model present in the data. The procedure is similarly extended to estimate the variance of white noise generating the autoregressive (AR) and moving average (MA) noise models. The method is validated on several seasonal time series data whose components are known a priori. 相似文献
9.
Dinh Tuan Pham 《Stochastic Processes and their Applications》1985,20(2):295-306
An extension of the linear Markovian repsentation called the bilinear Markovian representation is introduced, and is shown to provide representations of all-diagonal bilinear time series models. Some properties of the bilinear Markovian representation are also given. 相似文献
10.
How does innovation's tail risk determine marginal tail risk of a stationary financial time series? 总被引:6,自引:1,他引:5
We discuss the relationship between the marginal tail risk probability and theinnovation's tail risk probability for some stationary financial time series models. We firstgive the main results on the tail behavior of a class of infinite weighted sums of randomvariables with heavy-tailed probabilities. And then, the main results are applied to threeimportant types of time series models; infinite order moving averages, the simple bilineartime series and the solutions of stochastic difference equations. The explicit formulasare given to describe how the marginal tail probabilities come from the innovation's tailprobabilities for these time series. Our results can be applied to the tail estimation of timeseries and are useful for risk analysis in finance. 相似文献
11.
模型估计是机器学习领域一个重要的研究内容,动态数据的模型估计是系统辨识和系统控制的基础.针对AR时间序列模型辨识问题,证明了在给定阶数下AR模型参数的最小二乘估计本质上也是一种矩估计.根据结构风险最小化原理,通过对模型拟合度和模型复杂度的折衷,提出了基于稀疏结构迭代的AR序列模型估计算法,并讨论了基于广义岭估计的最优正则化参数选取规则.数值结果表明,方法能以节省参数的方式有效地实现AR模型的辨识,比矩估计法结果有明显改善. 相似文献
12.
Predictive control of nonlinear dynamic processes 总被引:1,自引:0,他引:1
R. Haber 《Applied mathematics and computation》1995,70(2-3):169-184
Predictive control can be applied if the reference value of the process is known in advance and the deterministic disturbances can be predicted. A cost function defined in the future horizon is minimized. The control signal is calculated for a control horizon, but only the first one is applied and the procedure is repeated (receding horizon strategy). Processes with mild analytical nonlinear characteristics are considered. The possible process models are either nonparametric (linear, Hammerstein, and Volterra weighting function series) or parametric ones (generalized Hammerstein, parametric Volterra, and bilinear models). The algorithms of the optimal and suboptimal predictive control based on the nonparametric and the parametric models mentioned are derived. Several simulations present how effective these methods are. The adaptive case is dealt with as well. 相似文献
13.
14.
ESTIMATION OF THE MIXED AR AND HIDDEN PERIODIC MODEL 总被引:4,自引:0,他引:4
何书元 《应用数学学报(英文版)》1997,13(2):196-208
ThisresearchissupportedbytheNationalNaturalScienceFoundationofChina.1.IntroductionGeneralizedhiddenperiodicmodelhasthefollowingformwhereacisthesetofallpositiveintegers,('~{((t);tEac}isastationarysequencewithzeromeanandcontinuousspectraldensity,i=n,qisanonnegativeinteger,'f=0,X=(Al,Az,',A,)isarealvectorwith--T相似文献
15.
Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology. 相似文献
16.
17.
潘家柱 《中国科学A辑(英文版)》2002,45(6):749-760
Discussed in this paper is the dependent structure in the tails of distributions of random variables from some heavy-tailed
stationary nonlinear time series. One class of models discussed is the first-order autoregressive conditional heteroscedastic
(ARCH) process introduced by Engle (1982). The other class is the simple first-order bilinear models driven by heavy-tailed
innovations. We give some explicit formulas for the asymptotic values of conditional probabilities used for measuring the
tail dependence between two random variables from these models. Our results have significant meanings in finance. 相似文献
18.
We present a series of related robust optimization models for placing sensors in municipal water networks to detect contaminants
that are maliciously or accidentally injected. We formulate sensor placement problems as mixed-integer programs, for which
the objective coefficients are not known with certainty. We consider a restricted absolute robustness criteria that is motivated
by natural restrictions on the uncertain data, and we define three robust optimization models that differ in how the coefficients
in the objective vary. Under one set of assumptions there exists a sensor placement that is optimal for all admissible realizations
of the coefficients. Under other assumptions, we can apply sorting to solve each worst-case realization efficiently, or we
can apply duality to integrate the worst-case outcome and have one integer program. The most difficult case is where the objective
parameters are bilinear, and we prove its complexity is NP-hard even under simplifying assumptions. We consider a relaxation
that provides an approximation, giving an overall guarantee of near-optimality when used with branch-and-bound search. We
present preliminary computational experiments that illustrate the computational complexity of solving these robust formulations
on sensor placement applications. 相似文献
19.
Zhao-jun Wang Yi Zhao Chun-jie Wu Yan-ting Li 《应用数学学报(英文版)》2006,22(2):219-226
There are already a lot of models to fit a set of stationary time series, such as AR, MA, and ARMA models. For the non-stationary data, an ARIMA or seasonal ARIMA models can be used to fit the given data. Moreover, there are also many statistical softwares that can be used to build a stationary or non-stationary time series model for a given set of time series data, such as SAS, SPLUS, etc. However, some statistical softwares wouldn't work well for small samples with or without missing data, especially for small time series data with seasonal trend. A nonparametric smoothing technique to build a forecasting model for a given small seasonal time series data is carried out in this paper. And then, both the method provided in this paper and that in SAS package are applied to the modeling of international airline passengers data respectively, the comparisons between the two methods are done afterwards. The results of the comparison show us the method provided in this paper has superiority over SAS's method. 相似文献