Linear growth model is widely used in the analysis and forecast of time series in economic and biological fields.
线性增长型模型被广泛应用于经济领域和对生物信号的时间序列的分析和预报。
Time series analysis based on neural networks theory cross through traditional frame of subjective model draw out prediction on the inner rules of linear time series data.
基于前向型神经网络理论的时间序列分析跳出了传统的建立主观模型的局限，通过时间序列的内在规律作出分析与预测。
In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed.
为了解决日益严重的城市交通问题，本文根据交通流已被证明的混沌特性，尝试采用非线性混沌模型来分析交通流时间序列。
The threshold autoregressive model is a kind of non-linear time series model recently established.
门限自回归模型是一种新近创立的非线性时间序列摸型。
Based on the time series, a model of linear artificial neural network is set and used for dynamic prediction of discharge of groundwater.
根据其时间序列，建立线性神经网络模型，并将其用于地下水流量的动态预测。
This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting.
该文介绍了内回归神经网络逼近非线性ARMA模型、用于时间序列预测的可行性。
ARCH model is a kind of dynamic non-linear time series model. It has widely used in the field of the finance and economic.
ARCH族模型是动态非线性的股票定价模型，它在金融和经济领域具有广阔的应用前景。
Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
基于混沌时间序列的局域线性预测模型，提出了多嵌入维的短期负荷预测方法。
Using linear regressive models (e. g. AR, ARMA model) to fit and predict the climatic time series, the results are not sufficiently good because there exist nonlinear variations in the time series.
在用AR、ARMA等线性模式对气候序列进行拟合和预报时，由于气候序列中存在着非线性变化，所以拟合和预报效果往往不太理想。
This article demonstrates that deformation forecast will be performed by a comprehensive method of non linear regression model combined with time series analysis.
本文将讨论综合运用非线性回归模型和时间序列分析的方法进行变形预报。
A new kind of fuzzy time series model is presented by using a fuzzy system of linear equations with fuzzy coefficients and real variables.
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。
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。
Most of the popular clustering methods are designed for the linear time series, assuming that the stationary time series can be fitted by linear model. In fact, the true word is nonlinear.
由于现实世界中时间序列多数是非线性的，而现有的时间序列聚类问题大多是基于线性时间序列模型进行聚类的，提出了可以用于非线性时间序列的聚类方法。
Most of the popular clustering methods are designed for the linear time series, assuming that the stationary time series can be fitted by linear model. In fact, the true word is nonlinear.
由于现实世界中时间序列多数是非线性的，而现有的时间序列聚类问题大多是基于线性时间序列模型进行聚类的，提出了可以用于非线性时间序列的聚类方法。
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