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1·In this paper, autoregressive moving average model (ARMA) is used to forecast the power load.

本文采用自回归滑动平均模型(ARMA)对电力负荷进行了预测。

2·Methods Using the Autoregressive Integrated Moving Average Model to fit the change of numbers of discharged patients.

方法 采用自回归移动平均模型对出院人次进行模型拟合。

3·We obtain some results as follows:In chapter 2, a new mixture autoregressive moving average model is proposed for modeling nonlinear time series.

提出了一类新的用于非线性时间序列建模的混合自回归滑动平均模型。

4·This paper uses the recursion algorithm of the least-square method to identify an autoregression model and an autoregression moving average model.

本文用最小二乘递推算法辨识自回归模型和自回归动平均模型，编制了电力系统短期在线负荷予报程序。

5·A mixed autoregressive moving average (MARMA) model is proposed for modeling nonlinear time series.

提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA)。

6·The theory of linear regression and the theory of moving average are applied to analyse single data in time series, the model of a linear moving self regression forecast are given out.

应用线性回归分析和移动平均理论，对按时间次序排列的单一数据序列，给出了一种线性移动自回归预测模型，并对原始数据受不确定因素影响而产生的随机振荡，给出了合理的控制区间和运行通道。

7·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。

8·The parameter estimation problem to the invertible vector moving average (ma) model essentially is a matrix spectral factorization problem.

可逆的向量滑动平均(MA)模型参数估计问题本质上是一个矩阵谱分解问题。

9·Methods The trend values were calculated and the monthly indexes were adjusted with moving average method of season prediction method, and then the model was applied.

方法 用季节预测法中的移动平均比率法计算趋势值与调整月指数 ，最后应用模型。

10·Then we use adaptive exponentially weighted moving average (AEWMA) control charts to control the process which satisfies the two-component model.

接下来就是进行过程监控方法的研究。本文主要对生产过程利用可适应性指数加权滑动平均(aewma)法进行质量控制。