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。
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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