共查询到20条相似文献，搜索用时 31 毫秒
1.
在地震子波非因果、混合相位的假设下,本文应用自回归滑动平均(ARMA)模型对地震子波进行参数化建模,并提出利用线性(矩阵方程法)和非线性(ARMA拟合方法)相结合的参数估计方式对该模型进行参数估计.在利用矩阵方程法确定模型参数范围的基础上,利用累积量拟合法精确估计参数.理论分析和仿真结果表明,该方式有较好的适应性：一方面提高了子波估计精度,避免单独使用矩阵方程法在短数据地震记录情况下可能带来的估计误差;另一方面提高了子波提取运算效率,降低了ARMA模型拟合方法参数范围确定的复杂性,避免了单纯使用滑动平均（MA）模型拟合法估计过多参数所导致的运算规模过大问题.初步应用结果表明该方法是有效可行的. 相似文献
2.
The determination of moving average (MA) models from a prior autoregressive (AR) approximation of a specified (target) spectral matrix is addressed; this is done in context with the need to simulate ground shaking and other natural phenomena as multivariate random processes. First, an existing technique based on a direct modeling of the target expression is revisited. In this regard, the influence of the order of the prior AR approximation, and the number of its harmonics used in the determination of the MA model, is described. Further, a simple selection technique of these parameters is presented that leads to an optimum MA approximation. Next, the relationship between a method based on the Cholesky factorization of the coveriance matrix and the present technique is investigated to derive additional insight into its convergence properties. Finally, an alternative modeling technique based on an AR representation of the inverse of the target spectral matrix is presented. 相似文献
3.
大跨度桥梁风场模拟方法对比研究 总被引：18，自引：4，他引：14
本文将基于线性滤波器的ARMA模型应用于大跨度桥梁的风场模拟,推导出自回归(AR)阶数P和滑动回归(MA)阶数q不等情况下,ARMA模型用于模拟多变量稳态随机过程的公式,将ARMA风场模拟方法与目前广泛应用于大跨度桥梁风场模拟的谐波合成法应用于一座实际大跨度斜拉桥的风场模拟,通过对比研究得出一些有意义的结论,并证实了ARMA法能够在保证模拟精度的前提下,大大提高风场模拟的效率。 相似文献
4.
为研究地震子波相位对反射系数序列反演的影响,在自回归滑动平均(ARMA)模型描述子波的基础上,提出采用z域对称映射ARMA模型零极点的方法构造了一系列相同振幅谱、不同相位谱的地震子波,并结合谱除法对人工合成地震记录进行反射系数序列反演.理论分析表明,子波相位估计不准时反射系数序列反演结果中残留一个纯相位滤波器,该纯相位滤波器的相位谱为真实子波和构造子波的相位谱之差.采用丰度和变分作为评价方法,在反演结果中确定出真实的或准确的反射系数序列.仿真实验和实际数据处理结果也验证了子波相位对反射系数序列反演的影响规律和评价方法的有效性,为进一步提高反射系数序列反演结果精度指明了研究方向. 相似文献
5.
E. Damsleth A. H. El-Shaarawi 《Stochastic Environmental Research and Risk Assessment (SERRA)》1988,2(1):61-72
The properties of the well known estimator of the transition probabilities in a binary time series are investigated. A formula for the variance is obtained, which generally involves a double integral. However, in the case when the binary series is obtained by hard clipping of an AR(1) process, a good and fairly simple approximation is derived. In the MA(1) or MA(2) case exact formulae for the variance is given. In the appendix an excellent approximation to the fourth order cumulant of a clipped AR(1) process is derived, which may be of interest in other applications as well. 相似文献
6.
Annual and monthly rainfall data generation schemes 总被引：2，自引：2，他引：0
Synthetic annual and monthly rainfall data series are generated by using autoregressive (AR) processes, Thomas-Fiering (TF) model, method of fragments (F) and its modified version (MF), two-tier (TT) model, and a newly developed wavelet (W) approach. It is seen that the W approach is as well in preserving the statistical behavior of the observed data series as the classical annual and monthly hydrological data generation schemes used in this study. The W approach is found even better in replacing some particular characteristics such as the mean of the sequence and correlation between the successive months in the series. It is, therefore, proposed as a new annual and monthly hydrological data generation scheme. 相似文献
7.
8.
Verification of ARMA identification for modelling temporal correlations of GNSS observations using the ARMASA toolbox 总被引：1，自引：0，他引：1
The classical least-squares (LS) algorithm is widely applied in practice of processing observations from Global Satellite Navigation Systems (GNSS). However, this approach provides reliable estimates of unknown parameters and realistic accuracy measures only if both the functional and stochastic models are appropriately specified. One essential deficiency of the stochastic model implemented in many available GNSS software products consists in neglecting temporal correlations of GNSS observations. Analysing time series of observation residuals resulting from the LS evaluation, the temporal correlation behaviour of GNSS measurements can be efficiently described by means of socalled autoregressive moving average (ARMA) processes. For a given noise realisation, a well-fitting ARMA model can be automatically estimated and identified using the ARMASA toolbox available free of charge in MATLAB® Central.In the preliminary stage of applying the ARMASA toolbox to residual-based modelling of temporal correlations of GNSS observations, this paper presents an empirical performance analysis of the automatic ARMA estimation tool using a large amount of simulated noise time series with representative temporal correlation properties comparable to the GNSS residuals. The results show that the rate of unbiased model estimates increases with data length and decreases with model complexity. For large samples, more than 80% of the identified ARMA models are unbiased. Additionally, the model error representing the deviation between the true data-generating process and the model estimate converges rapidly to the associated asymptotical value for a sufficiently large sample size with respect to the correlation length. 相似文献
9.
基于ARMA模型非因果空间预测滤波(英文) 总被引：3，自引：1，他引：2
常规频域预测滤波方法是建立在自回归(autoregressive,AR)模型基础上的,这导致滤波过程中前后假设的不一致,即首先利用源噪声的假设计算误差剖面,却又将其作为可加噪声而从原始剖面中减去来得到有效信号。本文通过建立自回归-滑动平均(autoregres sive/moving-average,ARMA)模型,首先求解非因果预测误差滤波算子,然后利用自反褶积形式投影滤波过程估计可加噪声,进而达到去除随机噪声目的。此过程有效避免了基于AR模型产生的不一致性。在此基础上,将一维ARMA模型扩展到二维空间域,实现了基于二维ARMA模型频域非因果空间预测滤波在三维地震资料随机噪声衰减中的应用。模型试验与实际资料处理表明该方法在很好保留反射信息同时,压制随机噪声更加彻底,明显优于常规频域预测去噪方法。 相似文献
10.
《Journal of Hydrology》1999,214(1-4):74-90
Four time series were taken from three catchments in the North and South of England. The sites chosen included two in predominantly agricultural catchments, one at the tidal limit and one downstream of a sewage treatment works. A time series model was constructed for each of these series as a means of decomposing the elements controlling river water nitrate concentrations and to assess whether this approach could provide a simple management tool for protecting water abstractions. Autoregressive (AR) modelling of the detrended and deseasoned time series showed a “memory effect”. This memory effect expressed itself as an increase in the winter–summer difference in nitrate levels that was dependent upon the nitrate concentration 12 or 6 months previously. Autoregressive moving average (ARMA) modelling showed that one of the series contained seasonal, non-stationary elements that appeared as an increasing trend in the winter–summer difference. The ARMA model was used to predict nitrate levels and predictions were tested against data held back from the model construction process – predictions gave average percentage errors of less than 10%. Empirical modelling can therefore provide a simple, efficient method for constructing management models for downstream water abstraction. 相似文献
11.
Nermin Sarlak 《水文研究》2008,22(17):3403-3409
Classical autoregressive models (AR) have been used for forecasting streamflow data in spite of restrictive assumptions, such as the normality assumption for innovations. The main reason for making this assumption is the difficulties faced in finding model parameters for non‐normal distribution functions. However, the modified maximum likelihood (MML) procedure used for estimating autoregressive model parameters assumes a non‐normally distributed residual series. The aim in this study is to compare the performance of the AR(1) model with asymmetric innovations with that of the classical autoregressive model for hydrological annual data. The models considered are applied to annual streamflow data obtained from two streamflow gauging stations in K?z?l?rmak Basin, Turkey. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
12.
ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO) 总被引：1，自引：0，他引：1
Ujjwal Kumar V. K. Jain 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):751-760
In the present study, a stationary stochastic ARMA/ARIMA [Autoregressive Moving (Integrated) Average] modelling approach has
been adapted to forecast daily mean ambient air pollutants (O3, CO, NO and NO2) concentration at an urban traffic site (ITO) of Delhi, India. Suitable variance stabilizing transformation has been applied
to each time series in order to make them covariance stationary in a consistent way. A combination of different information-criterions,
namely, AIC (Akaike Information Criterion), HIC (Hannon–Quinn Information Criterion), BIC (Bayesian Information criterion),
and FPE (Final Prediction Error) in addition to ACF (autocorrelation function) and PACF (partial autocorrelation function)
inspection, has been tried out to obtain suitable orders of autoregressive (p) and moving average (q) parameters for the ARMA(p,q)/ARIMA(p,d,q)
models. Forecasting performance of the selected ARMA(p,q)/ARIMA(p,d,q) models has been evaluated on the basis of MAPE (mean
absolute percentage error), MAE (mean absolute error) and RMSE (root mean square error) indicators. For 20 out of sample forecasts,
one step (i.e., one day) ahead MAPE for CO, NO2, NO and O3, have been found to be 13.6, 12.1, 21.8 and 24.1%, respectively. Given the stochastic nature of air pollutants data and in
the light of earlier reported studies regarding air pollutants forecasts, the forecasting performance of the present approach
is satisfactory and the suggested forecasting procedure can be effectively utilized for short term air quality forewarning
purposes. 相似文献
13.
The methods behind the predefined impulse response function in continuous time (PIRFICT) time series model are extended to cover more complex situations where multiple stresses influence ground water head fluctuations simultaneously. In comparison to autoregressive moving average (ARMA) time series models, the PIRFICT model is optimized for use on hydrologic problems. The objective of the paper is twofold. First, an approach is presented for handling multiple stresses in the model. Each stress has a specific parametric impulse response function. Appropriate impulse response functions for other stresses than precipitation are derived from analytical solutions of elementary hydrogeological problems. Furthermore, different stresses do not need to be connected in parallel in the model, as is the standard procedure in ARMA models. Second, general procedures are presented for modeling and interpretation of the results. The multiple-input PIRFICT model is applied to two real cases. In the first one, it is shown that this model can effectively decompose series of ground water head fluctuations into partial series, each representing the influence of an individual stress. The second application handles multiple observation wells. It is shown that elementary physical knowledge and the spatial coherence in the results of multiple wells in an area may be used to interpret and check the plausibility of the results. The methods presented can be used regardless of the hydrogeological setting. They are implemented in a computer package named Menyanthes (www.menyanthes.nl). 相似文献
14.
Ugo Moisello 《水文研究》2002,16(13):2667-2684
The maximum depth of a river section is schematized as a non‐stationary continuous‐parameter continuous stochastic process, with a three‐parameter lognormal distribution. Two processes, represented by a first‐order and a second‐order differential equation, are considered. Non‐stationarity is accounted for by the mean, the other parameters being assumed constant. The continuous processes are then discretized as AR(1) and ARMA(2,1) processes respectively, and used for computing the conditional probability (which is of practical interest) for a given maximum depth not to be exceeded in a period of given length. The models are applied to the River Po (Italy) and the AR(1) model is found to be preferable. An analysis of the effect of discretizing the parameter is also carried out, considering the second‐order model and the conditional probability, for which analytical results for the continuous‐parameter model are available. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
15.
16.
Real-time correction of water stage forecast during rainstorm events using combination of forecast errors 总被引：2，自引：2，他引：0
Shiang-Jen Wu Ho-Cheng Lien Che-Hao Chang Jhih-Cyuan Shen 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(4):519-531
This study proposes a real-time error correction method for the forecasted water stage using a combination of forecast errors
estimated by the time series models, AR(1), AR(2), MA(1) and MA(2), and the average deviation model to update the water stage
forecast during rainstorm events. During flood forecasting and warning operations, the proposed real-time error correction
method takes advantage of being individually and continuously implemented and the results not being updated to the hydrological
model and hydraulic routings so as to save computational time by recalibrating the parameters of the proposed methods with
real-time observation. For model validation, the current study adopts the observed and forecasted data on a severe typhoon,
Morakot, collected at eight water level gauges in Southern Taiwan and provided by the flood forecast system FEWS_Taiwan, which is linked with the reliable quantitative precipitation forecast (QPF) at 3 h of lead time provided by the Center Weather
Bureau in Taiwan, as the model validation. The results of numerical experiments indicate that the proposed real-time error
correction method can effectively reduce the errors of forecasted water stages at the 1-, 2-, and 3-h lead time and so enhance
the reliability of forecast information issued by the FEWS_Taiwan. By means of real-time estimating potential forecast error, the uncertainties in hydrology, modules as well as associated
parameters, and physiographical features of the river can be reduced. 相似文献
17.
Time series analysis of water quality parameters at Stillaguamish River using order series method 总被引：1，自引：1，他引：0
Farid Khalil Arya Lan Zhang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(1):227-239
This study applied the time series analysis approach to model and predict univariate dissolved oxygen and temperature time series for four water quality assessment stations at Stillaguamish River located in the state of Washington. The order series method was applied to fulfill the normality assumption for modeling the univariate time series. Then, the AR(I)MA models were applied to study the stationary and nonstationary time series, the Auto-Regressive Fractionally Integrated Moving Average model was applied to study the time series with long memory. The results showed there existed three different structures for the univariate water quality time series at Stillaguamish River watershed. The identified time series model for each univariate water quality time series was found to be capable of predicting future values with reasonable accuracy. Overall, the time series modeling approach may be an efficient tool in assessment of the water quality in the river system. 相似文献
18.
A smoothness priors-time varying autoregressive (AR) coefficient model method for the modelling of earthquake ground motion is shown. The method yields the instantaneous smoothed values of the AR coefficients and the instantaneous smoothed values of the innovations variance. These results in turn yield estimates of the instantaneous spectral density, the time varying covariance function and a simulation model for the ground motion data. An example of the application of the method to the analysis of an accelogram from the February 1971 San Fernando, California earthquake is shown. 相似文献
19.
Dong Yinfeng Li Yingmin Xiao Mingkui Lai Ming 《Soil Dynamics and Earthquake Engineering》2008,28(1):7-19
Some limitations of the Hilbert–Huang transform (HHT) for nonlinear and nonstationary signal processing are remarked. As an enhancement to the HHT, a time varying vector autoregressive moving average (VARMA) model based method is proposed to calculate the instantaneous frequencies of the intrinsic mode functions (IMFs) obtained from the empirical mode decomposition (EMD) of a signal. By representing the IMFs as time varying VARMA model and using the Kalman filter to estimate the time varying model parameters, the instantaneous frequencies are calculated according to the time varying parameters, then the instantaneous frequencies and the envelopes derived from the cubic spline interpolation of the maxima of IMFs are used to yield the Hilbert spectrum. The analysis of the length of day dataset and the ground motion record El Centro (1940, N–S) shows that the proposed method offers advantages in frequency resolution, and produces more physically meaningful and readable Hilbert spectrum than the original HHT method, short-time Fourier transform (STFT) and wavelet transform (WT). The analysis of the seismic response of a building during the 1994 Northridge earthquake shows that the proposed method is a powerful tool for structural damage detection, which is expected as the promising area for future research. 相似文献
20.
J. A. Vargas-Guzmán 《Stochastic Environmental Research and Risk Assessment (SERRA)》2012,26(6):765-776
Forecasting of extreme events and phenomena that respond to non-Gaussian heavy-tailed distributions (e.g., extreme environmental events, rock permeability, rock fracture intensity, earthquake magnitudes) is essential to environmental and geoscience risk analysis. In this paper, new parametric heavy-tailed distributions are devised starting from the exponential power probability density function (pdf) which is modified by explicitly including higher-order “cumulant parameters” into the pdf. Instead of dealing with whole power random variables, novel “residual” random variables are proposed to reconstruct the cumulant generating function. The expected value of a residual random variable with the corresponding pdf for order G, gives the input higher-order cumulant parameter. Thus, each parametric pdf is used to simulate a random variable containing residuals that yield, in average, the expected cumulant parameter. The cumulant parameters allow the formulation of heavy-tailed skewed pdfs beyond the lognormal to handle extreme events. Monte Carlo simulation of heavy-tailed distributions with higher-order parameters is demonstrated with a simple example for permeability. 相似文献