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 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。
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 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。
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 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 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。
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