The forewarning control limits and time series regression judgment model are built up based on the reliability series model in a non-repairable system and Shewhart control charts.
研究结果：利用不可修复可靠性串联模型与休哈特控制图建立预警控制界限，建立时间序列回归判别模型。
In this paper, a new model system is introduced, which synthetically applies time series model, nonlinear regression and combination forecasting model to forecast the change of the market price.
文章通过对一套市场价格预测模型体系的介绍，综合运用时间序列模型、多元非线性回归和组合模型来预测市场价格走势，探索从多角度综合预测市场价格的问题。
There are traditional model methods of forecasting short-term load, such as time series, regression analysis, and so on.
电力系统短期负荷预测使用的方法有传统建模方法，诸如时间序列、回归分析等方法。
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。
Finally, the results show the methods can effectively come into being regression analysis model of time-series data streams, and fulfill the prediction of future data streams.
最后，试验分析展示了研究结果能够有效地产生时间序列数据流的回归模型和实现数据流未来数据的预测。
Basically, the traditional way may involve method of time series, multi-linear regression and the Fourier expansion, etc.
传统方法中主要有时间序列法、多元线性回归法及傅立叶展开法等。
Fan J and Gijbels I gave the asymptotic normality of local polynomial regression estimation in dependent time series, where the weighted function is bounded.
对相依时间序列数据，在一定的条件下已有人证明了局部多项式加权回归系数估计服从渐近正态分布，其中核函数是有界的。
Using the methods of time series spectral analysis and Kalman filter, this article discussed the additive problems of two stochastic processes, mainly Auto Regression Moving Average (ARMA) processes.
本文利用时间序列谱分析和卡尔曼滤波的方法讨论了两个随机过程，主要是自回归滑动平均(ARMA)过程，的叠加问题。
Combining the advantages of regression analysis methods and time series forecast model with equal step length, a compound forecasting model was set up , and was tested with engineering data.
结果显示，把最小二乘支持向量机回归预测与等步长时序预测相结合的预测方法应用于地下工程围岩位移监测数据的分析及预测是可行的；
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。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
This means is fit for the time series forecast which difficult to judge the time series typical character. It don't need cost much time to search explaining variable as regression analysis.
该方法适合于对时间序列的典型特征难以做出判断的时间序列的预测，而且它也无需像回归分析中必须花费很多时间寻找解释变量。
The residual term in the regression model is the noise generated by the omitted variables that influent dependent variable in the model. The time series model can fit this noise.
回归模型的残差项反映了对被解释变量有影响但未列入解释变量的因素所产生的噪音，这部分噪音可由时间序列模型进行拟合。
First presented is a method of regression analysis combined with random time series technique for analysis of monitoring data of DAMS.
首先，叙述用回归分析与随机时间序列技术的组合方法来处理大坝的监测数据。
And for the natural factors, using segmentation of time series to divide levels, multiple linear regression is adopted to analyze the results.
对于自然因素，以时间序列来划分因素水平，用多元线性回归分析试验。
The time series ar (p) self-regression model is widely applied to prediction in economic field. Most of researchers and technicians in engineering are not familiar with it.
时间序列ar (P)自回归模型多应用于经济预测领域，工程技术界多数人对此不太热悉。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
A way of time series data mining was put forward based on the exploratory analysis and the mathematics module was founded by way of using linear regression technology.
提出了基于探索性分析的时序数据挖掘方法，采用线性回归技术建立了数学模型。
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。
This paper introduces the time series AR (p) self-regression model. and how to apply it to predict the output of B. F. gas in Iron and Steel Works.
本文介绍了时间序列ar (P)自回归模型以及如何利用它来预测高炉煤气的产量。
This essay combines to the characteristics of oil output, forecasts the crude oil output with time series modeling approach and regression analysis method and has a high precision.
本文结合石油行业产量的特点，运用SAS系统中时间序列建模方法及回归分析方法对原油月产量作了预测分析，预测结果精度较高。
The precision of mixed model considering plot random effects, time series error autocorrelation and different plantation density at one time is better than that of ordinary regression analysis method.
同时考虑样地的随机效应、观测数据的时间序列相关性及不同初植密度的混合模型模拟精度比传统的非线性回归方法模拟精度高。
There are many methods for medium- and long-term runoff forecasting, such as time series, multiple linear regression and so on, which often have deviation in forecasting precision.
径流中长期预报一直以来都是人们关注的热点研究问题，常用的时间序列法、多元回归分析法等都存在预报精度偏差过大的问题。
There are many methods for medium- and long-term runoff forecasting, such as time series, multiple linear regression and so on, which often have deviation in forecasting precision.
径流中长期预报一直以来都是人们关注的热点研究问题，常用的时间序列法、多元回归分析法等都存在预报精度偏差过大的问题。
应用推荐