First, time series models are used for prediction.
第一步使用时间序列模型进行预测研究。
Economic trend may be analyzed and forecasted, using by time series models.
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。
The course is an introduction to univariate and multivariate time series models.
本课程是对于单变量与多变量时间序列模型的一个介绍。
Chapter2: Traditional time series models and multivariate fuzzy time series models.
第二章、时间数列模型与多变量模糊时间数列模型。
Objective To explore the methods of disease index time series models with influencing factors.
目的探索带有影响因素的疾病指数时间序列建模方法。
In this paper, we present a nonparametric approach for checking the residuals of time series models.
提出一种时间序列模型残差诊断捡验的非参数方法。
In economic field, the time series models are important methods in describing and forecasting the objective economic process.
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。
The mining accuracy chart TAB cannot be used with time series models or with models that have continuous predictable attributes.
“挖掘准确性图表”选项卡不能用于时序模型或具有可预测连续属性的模型。
The prediction of stock market based on the artificial neural network has almost the same precision as that based on time series models.
通过人工神经网络得到的预测结果基本上与较传统的时间序列理论得到的预测结果精度相似。
The basic idea and some kinds of the common time series models and the development characteristics of time series are explained in detail.
详细阐明了时间序列的基本思想、几种基本时序模型和时序动态特征，讨论分析了如何进行模型识别、模型参数计算和模型的定阶。
The stability conditions and the existing conditions of limit cycle of AR-type nonlinear time series models are given. Some special models are discussed.
为此给出了AR型非线性时间序列模型的稳定性条件及极限环存在条件，并对一些特殊模型进行了讨论。
Using controlled time series models to reappear the stochastic wave in the automotive test is being researched. This method overcomes several defects of RPC.
用受控时间序列模型实现汽车实验中的随机波形再现是正在探讨的方法，该方法克服了RPC方法存在的几个缺陷。
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 course intends to meet two goals. It provides tools for empirical work with time series data and is an introduction into the theoretical foundation of time series models.
本课程意在达到两个目标：它提供了运算时间序列数据的工具并且对于时间序列模型的理论也会做基础的介绍。
It discusses and compares the forecasting models using neural networks and using time series.
讨论、比较了基于神经网络和基于时间序列的预测模型。
Secondly, time series trend analysis models in common use are introduced, whose illative process and applicability are also expatiated.
其次，研究了常用的时序数据趋势分析模型，并对它们的推理过程和适用性进行了详细的阐述。
Two kinds of models are derived; load prediction model based on building model recognition and load prediction model based on time series analysis.
提出了两种类型负荷预报模型，基于建筑模型辩识的负荷预报法和基于时间序列的负荷预报法。
By time series analysis, we build models depicting the cutting tool states, coacervate information from dynamic date and construct feature vectors for discrimination.
通过时间序列分析建立反映切削状态的数学模型，从动态数据中凝聚信息，构成用于判别的特征向量。
Because these models can reflect the feature of the financial market well, they have been widely applied in the time series analysis on financial data.
由于该模型被认为是最集中反映了金融市场数据方差变化的特点而被广泛应用于金融数据时间序列分析中。
The usual models predict the future exchange rate only based on the information provided by the history values such as time series modeling.
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。
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。
In the second part, the chaotic prediction models for hydrological time series are studied in terms of the chaotic characteristics of hydrological evolution process.
第二部分基于水文序列变化的混沌特性，对水文时间序列的混沌预测模型进行了研究。
These combination forecasting models are characterized with simple algorithm and comparison of forecasting effectiveness to different time series.
该模型具有计算简便的特点，而且具有可比性，能反映不同时间序列预测方法有效性。
At present, GARCH type models have been employed to model these high frequency financial time series due to their ability to capture the dynamic characteristics.
近年来GARCH模型被广泛地用于对变动频率很高的金融时间序列建模，它能较好地抓住此类时间序列的动态特征。
Aiming the square sum of error (SSE), we construct the algorithm to iterate and select an optimal parameter for optimizing the new models, which ADAPTS the model to time series more.
又以预测误差平方和SSE最小为目标，构造了优选并自动生成最佳平滑参数使平滑模型得以优化的最速下降算法，增强了指数平滑模型对时间序列的适应能力。
Founded on change speciality of series of dam safety monitoring forecast, artificial neural networks and nonlinear models of time series based on genetic algorithms are applied.
根据大坝监测数据在时序上变化特征，应用了神经网络和基于遗传算法的时间序列的非线性预测模型。
Threshold autoregressive models are widely used in time series applications.
门限自回归模型被广泛地用于许多领域。
One of the current forecast methods is time series forecast which constructs models according to the historical data before using it to forecast the future.
时间序列预测是预测领域内的一个重要研究方向，时间序列预测是一种根据历史数据构造时间序列模型，再把模型外推来预测未来的一种方法。
The five models used most often are oil futures prices, regression-based structural models, time-series analysis, Bayesian autoregressive models and dynamic stochastic general equilibrium graphs.
最常使用的五个模型是石油期货价格、回归结构模型、时间序列分析、贝叶斯自回归模型和动态随机一般均衡图。
What time series mention is the analytical technique to this kind of dependence. This requires found stochastic and dynamic models of time series.
时间序列分析所论及的就是对这种依赖性进行分析的技巧，这要求对时间序列数据建立随机动态模型。
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