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。
In this paper, the theory and method of fuzzy time series analysis are presented, the model form and the parameters estimate problem are studied.
本文提出了模糊时间序列分析的理论和方法，研究了模型形式及其参数估计问题。
This paper puts forward a seasonal neural network model to curve fitting analysis for nonlinearity and predict for the seasonal time series of outpatient amount.
本文提出一种利用季节性神经网络模型对医院门诊量进行非线性曲线拟合分析和预测。
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。
The Dynamic time series period analysis and prediction model analyses a serial-typed time series from the point of statistics, finding out the law. thereby succeeding in predicting the future.
动态时间序列周期分析预测模型是从数理统计的角度对值为连续型的时间序列进行分析，发现规律，从而成功预测未来。
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。
The disadvantage of establishing ARMA model with traditional time series analysis is analyzed; a new model building method based on judgment rules and long autoregression is put forward.
分析了传统时间序列分析法建立ARMA模型的不足，提出了一种利用模型阶数判断准则和长自回归法建模的新方法。
Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。
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。
On the basis of traditional time series analysis and modeling methods, the thesis puts forward a new complete and simply identification method by using ar model.
本文在传统时间序列分析建模方法的基础上，提出了用AR模型的新的完整而又简单的辨识方法。
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。
Furthermore, a random drift error model for IFOG is built by the method of time series analysis.
此外，采用时间序列分析方法，建立了IFOG的随机漂移误差模型。
For the features of disc proportioning system's lag and discharge rate's fluctuation, applying time series analysis, a disc discharge rate prediction model based on ar model was set up.
针对原料场圆盘配料系统下料量检测滞后和料量随堆料机进退变化较大的特点，应用时间序列分析方法建立了基于AR模型的圆盘下料量预测模型。
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。
According to the change pattern of some parameters in metal cutting processes, this paper proposes for the first time a new time series analysis model-Autoregressive Constant model ARC (2).
本文根据切削过程中一些参数的变化规律，从理论上首次提出了一种新的时间序列分析模型，即常系数固定价ARC(2)模型。
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；
On the basis of the model and by applying time series analysis, it is discovered that the site measurement series of continuously dynamic gas emission possesses fractal property.
在此基础上，应用时序数据分析法，对实测得到的钻孔瓦斯涌出量序列进行计算分析，发现其具有分维特征。
Combining Projection Pursuit(PP) and highdimensional time series analysis, the synthetic earthquake prediction model of highdimensional PP time series is built.
将投影寻踪（PP）与高维时间序列分析结合起来，建立了地震PP综合预测模型。
Using time series analysis methods, in this paper the prediction model of the epidemic encephalomyelitis in Heilongjiang Province were given.
本文用时间序列分析法建立了黑龙江省流脑预测模型。
Because of using the window technology with time-series analysis of DMU, so the new model is more realistic significance.
对具有时间序列的决策单元采用窗口分析技术，因此模型更具现实意义。
Finally, the random error model of HRG is established by using time series analysis method.
最后，采用时间序列分析方法建立了半球谐振陀螺的随机误差模型。
Based on random process theory and time series analysis, the paper advanced the adaptive combined smoothing model suiting to seasonality, trend and randomness of water consumption series.
利用随机过程及时间序列分析手段，根据用水量序列季节性、趋势性及随机扰动性的特点，建立了用水量预测的自适应组合平滑模型。
The statistical model with a time series analysis are discussed. And some examples are given to simulate the effects of these errors on the digitized data accuracy.
应用时间序列分析理论研究了数字化误差的数学模型，并且用实例计算模拟了数字化误差对数字化数据精度的影响。
ARMA model is one of the most common models in the modern time series analysis which is widely used in scientific researches and engineering systems.
ARMA模型是现代时间序列分析中最为常用的模型之一，在科学研究和工程系统中具有广泛的运用。
Objective the research aims to investigate the application of time series analysis method on time series datum, and establish forecasting model on cholecystitis incidence rate in Haixizhou region.
目的探讨时间序列分析方法在时间序列资料中的应用，建立海西州地区胆囊炎发病率的预测模型。
Finally, we give Bayesian analysis of the long memory time series ARFIMA model.
最后，对长记忆时间序列arfima模型进行了贝叶斯分析。
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。
The space of prediction and application of non-stationary time series were expanded through the combined model of wavelet analysis, gray and time series prediction methods.
将小波分析理论、灰色预测理论和时间序列预测法组合进行需水量的预测，为原始非平稳时间序列的预测应用拓展了空间。
In this paper, Auto Regressive model of metal content for specially monitored metals in lubricating oil monitoring system of aeroengine is built by the method of Time Series Analysis.
利用神经网络方法对某型航空发动机滑油监控系统中需重点监控的金属元素含量建立了网络，并根据该模型对其含量变化趋势进行了预测分析。
In this paper, Auto Regressive model of metal content for specially monitored metals in lubricating oil monitoring system of aeroengine is built by the method of Time Series Analysis.
利用神经网络方法对某型航空发动机滑油监控系统中需重点监控的金属元素含量建立了网络，并根据该模型对其含量变化趋势进行了预测分析。
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