Estimating these models , and fitting sample data lead to a conclusion that the smooth transition autoregressive model is the best one which describes the rmb ' s real exchange rate behavior well 通过对这些模型的估计，以及在所估计的模型的基础上对样本数据进行拟合，发现了最适合中国人民币实际汇率的动态行为的模型，进而对人民币实际汇率的行为进行更深入的分析和讨论。
In the last decade , there exist two active lines on the investigation of nonlinear time series . one is the autoregressive conditional heteroscedasticity ( arch ) model , the another is the nonstationary ( unit root ) time series model 对非线性时间序列的研究，近几十年来，有两条研究路线非常活跃，其一是自回归条件异方差( arch )模型，其二是非平稳(单位根)时间序列模型。
The autoregressive conditional heteroskedastic ( arch ) class of models for conditional variances was put forward by engle ( 1982 ) proved to be extremely useful for analyzing economic time series . garch models have been developed to account for empirical regularities in financial data Engle ( 1982 )提出的arch模型，对经济时间序列中的条件方差分析十分有用， arch模型可以很好地刻划金融数据。
By using autoregressive integrated moving average model and on the basis of chinese textile and clothing export data from january of 2000 to december of 2004 , this paper carries out forecast analysis for the chinese textile clothing export tendency of 2005 and 2006 摘要本文利用单整自回归移动平均模型，依据2000年1月至2004年12月中国纺织品服装出口额数据，对2005年和2006年中国纺织品服装出口走势进行预测分析。
We consider a causal , stationary , autoregressive , moving average [ arma ( p , q ) ] process with heavy tailed noise variables . we develop the definition of the inverse autocorrelation function , and obtain the g - spectral estimator of the inverse autocorrelation function 本文基于噪声序列具有重尾分布的因果、平稳自回归滑动平均[ arma ( p ， q ) ]过程，给出了其逆自相关函数的定义，并且给出了逆自相关函数的g -谱估计。
Autoregressive model can predict the low - scale ( high frequency random ) data as well as fnn , but it is simpler than fnn . so we use ar to predict high frequency data . we perform simulations for a certain 30s sttf prediction using different approaches and compare the results 对于分解后的高频随机数据，自回归( ar )模型预测能取得和模糊神经网络相当的精度，但它相对而言数学物理意义更明确，不需要确定繁杂的模糊规则。
The corresponding models of realized volatility and realized covariance of time series of high - frequency finance are brought forward and the realized volatility - autoregressive and moving average ( rv - arma ) model and the realized volatility - vector autoregressive ( rv - var ) model are set up 摘要对高频金融时间序列的“已实现”波动和“已实现”协方差提出相应的模型并建立“已实现”波动自回归移动平均模型和“已实现”波动向量自回归模型。
In this thesis , autoregressive moving average ( arma ) models are applied to identify the flow regimes of two - phase flow . experiments are carried out on the facilities include gas - solid fluidized bed and gas - liquid two - phase flow pipelines . the results show that the methods are effective 本文基于arma ( autoregressivemovingaverage )模型，对气固流化床和气液两相流系统进行了分析并进一步对其流型进行了辨识，得到了一些有益的结论。
Parameter method was adopted to generate time series conforming to the specified power spectrum . a autoregressive model was estimated by program which based upon yule - walker equation . good agreement was obtained between the simulated spectrum and the target spectrum 本文利用ar参数模型方法根据阵风功率谱模拟产生相应的风速时间序列，编写了相应的matlab模拟程序，并计算得到了ar模型的参数，结果表明模拟信号的功率谱与原始谱比较一致。
Based on these models , the differential pressure fluctuation signals of gas - liquid two - phase flow under different flow regimes in various diameter pipelines are analyzed . experimental results show that the autoregressive orders of bubble flow and annular flow are bigger than that of slug flow 对不同管径不同流型下的差压波动信号进行了建模，在此基础上分析得出：弹状流流型下arma模型的阶数n最小，泡状流和环状流流型下的arma模型阶数n较大。
