Using the methods of time series spectral analysis and Kalman filter, this article discussed the additive problems of two stochastic processes, mainly Auto Regression Moving Average (ARMA) processes.
本文利用时间序列谱分析和卡尔曼滤波的方法讨论了两个随机过程，主要是自回归滑动平均(ARMA)过程，的叠加问题。
This paper USES the moving average trading rule of the technical analysis method to do predictability research.
采用移动平均线的技术分析方法进行了可预测性研究。
An algorithm of feature extraction and damage alarming based on auto-regressive moving-average (ARMA) time series analysis is presented.
本文基于时间序列分析ARMA模型探讨了结构损伤特征提取和损伤预警的实现方法。
Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。
Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。
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