时间序列模型在建筑物沉降监测中的应用

简要介绍了时间序列分析的基本原理及方法、数据识别、建模和预报及其在变形监测数据处理中的应用。结合华景新城二区六期20号楼的一个监测点（J101）连续20期的观测数据进行了计算分析,实际工程应用表明,该模型能准确监测和预报建筑的变形,具有很强的实际应用价值。     

利用ARMA模型改进GPS高程时间序列的研究

ARMA可用于时间序列建模,本文利用ARMA模型改进了部分IGS连续跟踪站GPS高程时间序列。计算了改进前后时间序列的线性速度,可以发现时间序列线性速度变化不明显,但精度却提高很多。通过研究表明利用ARMA模型有利于降低GPS高程时间序列噪声,可用于GPS高程时间序列的分析和研究。     

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EFFICIENT ITERATIVE ARMA APPROXIMATION OF MULTIVARIATE RANDOM PROCESSES FOR STRUCTURAL DYNAMICS APPLICATIONS

An efficient Auto-Regressive Moving–Average (ARMA) approximation method is presented for simulating stationary random processes with specified (target) power spectra in conjunction with structural dynamics applications. It involves an iterative algorithm developed for minimizing a physically motivated ‘energy’ measure, in the frequency domain, of the ARMA approximation of an AR representation of the target spectrum. The iterative algorithm can be used to adjust, for better spectral matching, the parameters of an arbitrary ARMA approximation of the random process determined by any other method; this is accomplished without increasing the requisite order of the ARMA approximation. The efficiency of the proposed method is demonstrated by considering spectra which are commonly used in earthquake engineering and ocean engineering.     

地磁秒数据补齐策略探讨

通过研究湖北省地磁台网数据发现,相邻台站数据相关性高且趋势相同.为解决地磁秒数据短期缺数的问题,基于素描的想法结合地磁特性,提出一种新的补数策略,即用傅里叶拟合法拟合出相似度高的完好数据作轮廓,调整傅里叶参数用轮廓拟合缺失数据,再用ARMA预测模型对细节进行修复.通过对实际缺省的地磁数据进行处理,验证了该策略具有良好的...     

Time-series modeling of reservoir effects on river nitrate concentrations

Saylorville Reservoir is a 24.1 km² impoundment of the Des Moines River located approximately 10 km north of the City of Des Moines, Iowa, USA. Surface water from the Des Moines River used for drinking water supply is impaired for nitrate–nitrogen. Monthly mean nitrate concentration data collected upstream and downstream of the reservoir for a 30-year period (1977–2006) were selected for time-series analysis. Our objectives were to (1) develop a model describing nitrate concentrations downstream of the reservoir as a function of the concentrations entering the reservoir and (2) use the model to provide a 1-month ahead forecast for downstream water quality. Results indicated that downstream nitrate can be effectively modeled using a transfer function approach that utilized inflow concentrations during the current and previous month as input variables. Inflow concentrations were modeled using an AR(20) model, with the higher order model consistent with temporal correlation noted by others. The transfer function model suggested that the reservoir is reducing nitrate concentrations by 22 ± 6%, a reduction that greatly exceeds previous estimates. Monthly nitrate forecasted with the model were nearly all within a 95% prediction interval of their actual measured values and did not appear greatly affected by flow variations.     

基于ARMA模型的BDS卫星钟差异常值探测及其短期预报

星载原子钟在运行过程中会受到恶劣空间环境与设备老化等因素的影响,使得卫星钟差数据中经常存在异常值,其中AO(additive outlier)类异常值是钟差序列中常见的一类异常值.结合最大期望算法与自回归滑动平均(autoregressive moving average, ARMA)模型,提出一种AO类异常值探测算法...     

基于ARMA模型判别气压传感器故障

我国现有的自动气象站设备缺乏能够反映其自身运行状态的参数信息,导致对设备运行监控能力不足,无法快速定位设备故障。为了判别气压传感器的工作状态,从观测数据出发,提出了基于ARMA模型的气压预测方法,利用已测气压值,对当前时刻气压值进行合理的预测,并给出该预测值的置信区间。利用气压传感器的实测值与此预测值进行比较,如果实测值偏离了该预测值的置信区间,可以认为气压传感器或相关数据采集单元出现故障。通过实例计算和比较,模型ARMA(2,1)对于自动气象站气压值的一步预测误差为0,具有较好的预测效果,可以有效判别气压传感器的工作状态。     

高边坡变形非线性时变统计模型研究

基于高边坡长期变形监测资料,深入分析了边坡变形的影响因素,建立边坡非线形时变统计监控模型,并将模型应用于锦屏一级水电站边坡工程。通过典型测点长期监测资料的分析,确定时效和降雨是影响边坡变形的2个主要因素,并给出了各自的表述形式,在Matlab平台上,采用非线性回归方法得到边坡非线性时变统计模型的参数。在此基础上,根据边坡变形拟合残差时序,采用ARMA模型方法进行拟合和预测,对边坡非线性时变模型的拟合和预测进行修正。结果表明,模型能较好地描述高边坡的变形特征,并且具有很好的拟合和预测精度。     

基于高阶累积量ARMA模型线性非线性结合的地震子波提取方法研究

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.