First presented is a method of regression analysis combined with random time series technique for analysis of monitoring data of DAMS.
首先，叙述用回归分析与随机时间序列技术的组合方法来处理大坝的监测数据。
A time series data set is a sequence of random variables indexed by time.
时间序列数据是以时间为指标的一个随机变量序列。
According to the character of non linear network traffic, the traffic time series is decomposed into trend component, period component, mutation component and random component.
文章考虑网络流量非线性的特点，通过不同的数学模型将流量时间序列分解成趋势成分、周期成分、突变成分和随机成分。
The random settlement could be gotten by random prediction model that is established by smooth and stable time series analysis method.
用平稳时间序列分析方法建立随机部分模型，并预测沉降随机部分值，二者之和即为某时期沉降预测值。
The killing probability in finite random stationary time series is studied by stochastic passage theory.
应用随机穿越理论分析了有限个随机滞留时间序列中的毁伤概率问题。
The reliability of the time-series analysis method in processing unsteady random signals is verited.
验证了时间序列分析方法在非平稳随机信号处理方面的可靠性；
By analysing the same data with BDS Statistic, I take the research of random characteristic in economic time series.
对同样的数据进行BDS分析，研究了经济时间序列的随机特性。
Electricity load can be seen as a series of random sequences, their characteristics can be analyzed by time series.
电力负荷可看作是一系列随机序列，其特性可利用时间序列方法来分析。
The reconstruction of vehicle random vibration signal under different vehicle speed and road conditions is realized in laboratory, based on an auto regressive moving average (ARMA) time series model.
基于自回归滑动平均模型(ARMA)的方法，实现了不同车速和路面条件下的随机振动信号的实验室再现。
Combined with certain type time series recount multiplicity model and random type ARMA model, establish the time series model of the death rate in Chongqing urban area.
应用确定型的时间序列分解法乘法模型与随机型的arma模型相结合，建立重庆市主城区人口死亡率的时间序列模型。
Finally, the random error model of HRG is established by using time series analysis method.
最后，采用时间序列分析方法建立了半球谐振陀螺的随机误差模型。
In this article, the random mathematical method of surface roughness is given with modern time series theory.
本文运用现代时间序列理论，建立了表面粗糙度轮廓的随机数学模型。
The example result shows that the random-sets method is a nice approach to time series analysis.
实例结果分析表明，随机集模型是一种很好的时间序列分析方法。
Size curves of cocoon filaments can be regarded as non-stationary time series with finite length varying at random.
茧丝纤度曲线的预测研究茧丝纤度曲线可视为是长度有限且随机变动的非平稳时间序列。
Furthermore, a random drift error model for IFOG is built by the method of time series analysis.
此外，采用时间序列分析方法，建立了IFOG的随机漂移误差模型。
Based on random process theory and time series analysis, the paper advanced the adaptive combined smoothing model suiting to seasonality, trend and randomness of water consumption series.
利用随机过程及时间序列分析手段，根据用水量序列季节性、趋势性及随机扰动性的特点，建立了用水量预测的自适应组合平滑模型。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
Conclusions Moving seasonal mean ratio method could consider secular, seasonal, cyclic and random tendencies of time-series data together and could serve as a useful tool for prediction.
结论移动平均比率法综合考虑长期、季节、周期及随机趋势，预测效果较好。
The series of dynamic measuring errors of the predicting correction of real-time errors of gray model is also a random process and has these four basic characteristics.
灰色模型实时误差预报修正的动态测量误差序列同样为随机过程，也具有上述基本特性。
The series of dynamic measuring errors of the predicting correction of real-time errors of gray model is also a random process and has these four basic characteristics.
灰色模型实时误差预报修正的动态测量误差序列同样为随机过程，也具有上述基本特性。
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