时间序列模型主要是自回归模型。
第一步使用时间序列模型进行预测研究。
提出一种时间序列模型残差诊断捡验的非参数方法。
In this paper, we present a nonparametric approach for checking the residuals of time series models.
本文主要讨论双线性时间序列模型的平稳性与可逆性。
In this paper, we discuss the stationarity and invertibility of a bilinear model.
本课程是对于单变量与多变量时间序列模型的一个介绍。
The course is an introduction to univariate and multivariate time series models.
建立一种基于结构时间序列模型的新的时间序列季节调整方法。
In the paper, we construct a new seasonal adjustment method of time series on the basis of the structural time series model.
应用拉氏变换和Z变换间关系，得到系统的ARMA时间序列模型。
By the relationship of Laplace transform and Z-transform, the ARMA time series model for this system is obtained.
随机波动(SV)模型是一种重要的具有隐性波动的时间序列模型。
The Stochastic Volatility models (SV model) is a kind of time series model which can reflect fluctuation that can not be observed directly.
门限自回归模型（TAR）是一种分段线性的非线性时间序列模型。
Threshold autoregressive model (TAR) is a nonlinear sequential model which is segmentedly linear.
应用方差分析和时间序列模型建立了一套新的燃气轮机性能监测方法。
A new set of gas turbine performance monitoring methods has been established by using variance analysis and time sequence models.
结果表明，基于时间序列模型的卡尔曼滤波器有效地减小了随机误差。
The results show that the filter based on the time sequence model can effectively decrease the random error.
利用时间序列模型不仅能够分析经济序列的趋势，同时还可以进行预测。
Economic trend may be analyzed and forecasted, using by time series models.
利用模糊系数实变量的线性方程组建立了一种新的模糊随机时间序列模型。
A new kind of fuzzy time series model is presented by using a fuzzy system of linear equations with fuzzy coefficients and real variables.
提出了DNA序列的时间序列模型，给出了DNA序列整数序列的一个映射。
A time series model of DNA sequence is proposed. A map of DNA sequence to integer sequence is given out.
最后，运用AR自回归时间序列模型对未来两年甘肃省金融风险状况进行预测。
Finally, I predict financial risk situation for the next two years in Gansu province by use of ar autoregressive time series model.
金融时间序列模型的变点分析是一类重要的统计问题，它引起众多学者的关注。
The change-point analysis in financial time series has been regarded as one of the core areas of research in statistics.
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。
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.
可运用时间序列模型中的曲线拟合方法，对重庆入境旅游人数和旅游收入进行预测。
Curve Fitting Method of Time Series Model can be used to predict the number of inbound tourists and tourism revenue of Chongqing.
在经济领域中，运用时间序列模型来进行客观经济过程的描述和预测是一个非常重要的方法。
In economic field, the time series models are important methods in describing and forecasting the objective economic process.
根据对一类时变时间序列模型结构特点的研究，提出了一种时变ar模型的递推参数估计算法。
According to the structure characteristics of a time varying time series model, a new recursive parameter estimation algorithm of the time varying ar model is proposed.
在对激光陀螺漂移数据建立时间序列模型的基础上，对激光陀螺的漂移数据进行了卡尔曼滤波。
The paper sets up a time sequence model of laser gyro random error and processes the drift data by Kalman filter based on the model.
固有模型只是根据汇率的历史值所提供的信息预测未来的现汇汇率。如广泛应用的时间序列模型。
The usual models predict the future exchange rate only based on the information provided by the history values such as time series modeling.
为将基于窗谱估计的模型验证技术应用于金融时间序列领域，以解决金融时间序列模型的设定正确性。
Thus, the window spectrum estimation technique provides more effective model validation than the traditional back-test m.
传递函数———噪声模型是一类多变量时间序列模型，它在表达系统动态影响机制方面有着独到的优势。
The transfer function-noise model is a kind of multivariate time series model which has much advantage in expressing dynamic mechanism of a system.
许多经济学家们不懈努力，孜孜以求，试图找到一个能够全面地刻划金融数据这些特性的时间序列模型。
Many economists keep on working hard, making a great effort to try to find a time series model which can capture most of these characteristics of financial data.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
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