This conclusion indicates that under the indirect measurement of earnings persistence (the estimation of θ), earnings time series parameters can not explain ERC, while under the direct measurement of earnings persistence (the estimation of φ〓 and φ〓), earnings time series parameters can explain ERC in some degree, and the lower-order autoregressive coefficient can explain the ERC better than the high-order one.
该结论说了间接计量盈余持续性法下，盈余时间序列参数对ERC没有解释力度；但在直接计量盈余持续性法下，盈余时间序列参数对ERC有一定的解释意义，并且低阶次的自相关系数比高阶次的自相关系数具有更强的解释力。
Taking advantage of scattered point diagram, the way of judging the station- nary property of the fuzzy random time series is presented; Three kinds model of lin- ear fuzzy random梩he fuzzy autoregressive model, the fuzzy moving average m- odel and the fuzzy autoregressive moving average model are introduced; The appro- aches of recognition, determining order, parameter estimation and examination of th- ese models are presented. An application example is given in light of the fuzzy auto- regressive model.
利用散点图给出了对模糊随机时间序列进行平稳性判断的方法；建立了三种线性模糊随机模型——模糊自回归模型、模糊滑动平均模型和模糊自回归—滑动平均模型；给出了这三种模型的识别、定阶、参数估计和检验方法；讨论了某些非平稳模糊随机时间序列；给出了模糊自回归模型的应用实例。
An information fusion technology was successfully coupled to the analysis of original weak electrical signals in Bellis perennis, which consisted of a touching testing using platinum sensors in a system of self|made double shields, the signals denoised by the wavelet soft threshold and a forecast system of time series by the autoregressive integrated moving average constructed with plant characters of electrical signals.
将自制的具有铂金传感器的双重屏蔽微弱电信号测试系统，所获取的植物微弱电信号、小波软阈值消噪、时间序列的求和自回归移动平均模型等单因素分析耦合成信息融合技术，对雏菊电信号时间序列进行了数据融合分析预测。
By the modern time series analysis method, based on the autoregressive moving average innovation model and augmented state space model, under the linear minimum variance optimal fusion rule weighted by scalars, a multisensor optimal distributed fusion Wiener filter is proposed for single channel ARIVIA signals with white and colored measurement noises.
应用现代时间序列分析方法，基於自回归滑动平均(autoregressive moving average，ARMA)新息模型和增广的状态空间模型，在线性最小方差按标量加权最优融合规则下，对带白色和有色观测噪声的单通道ARMA信号，提出了多传感器最优分布式融合Wiener滤波器。
Traditional autoregressive moving-average model can`t be applied directly medium-term forecast on time series because noise occurs and accumulates with time.
由于噪声的存在并随时间累积，传统的自回归滑动平均模型不能直接应用于时间序列的中期预测。
The generalized autoregressive conditional heteroscedasticity model has the ability to describe the volatility of time series.
广义自回归条件异方差模型具有描述时间序列波动性的能力。
This paper discusses the application of time series analysis method in random road surface description. First, a road surface model of autoregressive is established for measuring data of tank road profile based on the time series analysis.
该文探讨时间序列分析方法在随机路面描述中的应用，对坦克行驶道路路面的实际测量值，应用时间序列分析的方法，建立了一个自回归（Autoregressive）模型。
By wavelet-vectors and AR-model of2-dimension autoregressive time series, we give an identification algorithm of the orbits of shaft centerline.
基于小波向量和自回归AR模型给出了大型旋转机械轴心轨迹的一种识别方法。
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。
Then, an observer bank of autoregressive time series models based on multi-component neural-network architecture is used for model diagnosis of rotor fault vibration signals.
最后根据该方法组成了一个自回归时间序列模型库，用于转子故障的模型诊断中。
The results show that the time series change of monthly runoff can be represented by the simple, seasonal, first order autoregressive model and Skewing change of random term. The space change of monthly runoff can by expressed by main station model.
结果表明简单的季节性一阶自回归模型加上随机项的偏态变换可用来表征月径流的时序变化，主站模型可用来表征月径流的空间变化。
The article focuses on the sum of the calendar year real estate sales price index data for statistical analysis using time series of real estate sales price index autoregressive integrated moving average time to model and predict.
文章重点是通过对我国历年房地产销售价格指数数据进行统计分析，利用时间序列建立我国房地产销售价格指数的求和自回归移动平均时间模型，并进行预测。
Using the modern time series analysis method, based on the controlled autoregressive moving average (CARMA) innovation model, two new algorithms of steady state Kalman filter gain for stochastic control systems are presented, where the solution of the Riccati equation is avoided.
应用现代时间序列分析方法，基于受控的自回归滑动平均（CARMA）新息模型，提出了随机控制系统稳态Kalman滤波器增益的两种新算法，避免了求解Riccati方程。
This paper discusses the monitoring of cutting tool wears, modeling an autoregressive of time-series and using linear discriminant function to classification analysis. Experiments prove the method proposed is available.
本文用时间序列的分析方法对刀具切削中的声发射信号建立自回归模型，用线性判别函数进行类别分析实现对工具的监视实验证明，所用方法是可行的。
In the basis of that, the autoregressive model, which is very mature in the theory of linear time series, was used for establishing the future cash dividend predicted model. Parameters were estimated by computer simulation.
在这个基础上，本文利用线性时间序列理论上十分成熟的自回归模型（AR）建立了未来现金股利的预报模型，并通过计算机模拟技术对参数进行了估算。
An autoregressive model is used to fit the linear part of series; the neural network is used to fit the nonlinear part of series and to compensate the unknown disturbance.
利用一个线性AR模型拟合时间序列的线性部分，用神经网络拟合时间序列的非线性部分并补偿外界未知的扰动。
Autoregressive integrated moving average models (ARIMA) has been widely used as a famous liner time series model;
自回归求积移动平均（ARIMA）模型是一种目前应用广泛最的线性时间序列建模工具之；
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）。
Time series analysis is a method of studying stochastic data series by using mathematics statistics and stochastic process theories. It generally makes use of model methods to carry out, such as autoregressive (AR) model, autoregressive moving average (ARMA) model.
时间序列分析是用数理统计和随机过程理论研究随机数据序列的方法，通常是利用模型法实现的，如自回归（AR）模型、ARMA模型等。
The corresponding model of realized covariance matrix of in this paper vector high-frequency financial time series is brought forward and the realized vector autoregressive model is set up is this paper.
对向量高频时间序列的已实现协方差阵提出相应的模型并建立了已实现向量自回归模型。