Stationary time series state space modeling method for the analysis of the transition process gyro.
将非平稳时间序列的状态空间建模方法用于陀螺过渡过程的分析。
The methods for fitting the autoregressive model to the stationary time series are briefly reviewed.
本文首先略述用自回归模式去拟合平稳时间序列的各种方法；
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。
For multiple stationary time series Granger causality tests and vector autoregressive models are presented.
多平稳时间序列，“格兰其”成员因果律测试和自回归模式给的矢量。
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。
Through ARIMA model and standardization, the non stationary vibration series acquired in the field were transformed to stationary time series normally distributed.
将现场测得的非平稳振动序列通过ARIMA模型和标准化处理，转化成标准正态平稳时间序列。
The space of prediction and application of non-stationary time series were expanded through the combined model of wavelet analysis, gray and time series prediction methods.
将小波分析理论、灰色预测理论和时间序列预测法组合进行需水量的预测，为原始非平稳时间序列的预测应用拓展了空间。
The statistical model of frequency and intensity of anomalous microtherm events in Nanjing is established by means of the extreme value distribution theory of stationary time series.
本文借助于平稳时间序列的极值分布理论，对南京地区异常低温事件频次和强度建立统计模型。
Most of the popular clustering methods are designed for the linear time series, assuming that the stationary time series can be fitted by linear model. In fact, the true word is nonlinear.
由于现实世界中时间序列多数是非线性的，而现有的时间序列聚类问题大多是基于线性时间序列模型进行聚类的，提出了可以用于非线性时间序列的聚类方法。
The aim of this paper is to give a systematic account of asymptotic properties of the sample autocovariance, autocorrelation and partial autocorrelation functions of linear stationary time series.
本文的目的在于，对于线性平稳时间序列的样本、自协方差、自相关和偏相关函数的渐近性质，给出一个比较系统的描述。
The real signals have often non-stationary characteristic, so if we analyse these time series using AR model directly, we cant obtain design result.
由于实际信号常常具有非平稳特征，直接应用AR模型进行时间序列分析，得不到理想的效果。
Objective:This paper gives a statistical method of how to test if a time series is stationary.
目的：提出一种客观的统计检验方法来判断时间序列的平稳性。
EMD method is a new method for analyzing nonlinear and non-stationary data, which has more advantage than wavelet analysis, and it can process short time series precisely.
EMD方法是对非平稳、非线性信号进行分析的一种新的时频分析方法。它比小波分析等方法具有更强的特性并能准确地处理非常短的数据序列。
EMD method is a new method for analyzing nonlinear and non-stationary data, which has more advantage than wavelet analysis, and it can process short time series precisely.
EMD方法是对非平稳、非线性信号进行分析的一种新的时频分析方法。它比小波分析等方法具有更强的特性并能准确地处理非常短的数据序列。
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