时间序列预测模型在生产性行业里具有广泛的作用。
Time serial predicting model can act well in many industries.
目的建立一种新的具有抗噪声能力的神经网络时间序列预测模型。
Aim To construct a new time-series forecasting model based on neural network with the capability of noise immunity.
目的：探讨ANN时间序列预测模型在疾病发病率或死亡率预测上的应用前景。
Objective: To explore the prospect of predicting disease incidence of the predictive model of nonlinear time series by BP neural network.
从信息论角度出发，利用神经网络非线性时间序列预测模型，构造了油田产油量、产水量的多维时间序列神经网络预测器。
In this paper, using neuron network models of nonlinear multidimensional time series prediction, neuron network predictors for the oil production and water production of oil fields were constructed.
基于该网络的时间序列预测模型可以实现性能优越的非线性预报器，将其应用于非线性系统的故障预报能够取得良好的效果。
The LMBP neural network can predict nonlinear time series very well and the new method is effective for the fault prediction of nonlinear systems.
建立了一个时间序列的门限自回归的预测模型，为股票市场的非线性研究这一前沿领域作了一点新的尝试。
Set up one time door limit prediction model of autoregression of array, study for nonlinearity of stock market this front field make new try a bit.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
讨论、比较了基于神经网络和基于时间序列的预测模型。
It discusses and compares the forecasting models using neural networks and using time series.
本文引入灰色系统理论，利用有限的时间序列，按照GM(1,1)建模方法，建立起黑龙江省污水总量长期预测模型。
This paper inserts grey system, makes use of finite time series, follows GM (1, 1) building method, builds the long term prediction model of total waste -water in Heilongjiang Province.
并用灰色预测理论对该系统现有的年用电量时间数据序列进行处理，进而建立了GM（1，1）预测数学模型，最后提供了预测实例。
Time data sequence on existing annual power consumption are dealt with by using principles of grey production, thus establishing GM(1, 1) grey model. Finally, a example is given.
针对其时间序列的特点，研究了ARIMA的不同模式，提出了面向特定市场的ARIMA模型，及其预测和估计方法。
Aim at the trait of time series, investigate the different ARIMA patterns, put forward the ARIMA model and forecast and estimate aim at special market.
本文介绍了电子对抗中对混沌时间序列进行预测的一种数学模型和一种基于混沌序列的反侦察系统。
In this paper, the prediction of mathematical model for chaotic sequence in electronic antagonism and the anti-reconnaissance based on chaotic sequence are presented.
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。
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.
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。
This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting.
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。
Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
在路基填筑施工过程中，根据沉降观测数据用时间序列分析方法建立等维信息动态预测模型。
During the filling construction of the roadbed the total settlement value could be predicted by using time series equal interval prediction model of recent information.
基于变分贝叶斯及相空间重构理论，提出了含噪混沌时间序列相空间域线性回归预测模型。
We present a linearly regressive prediction model for noisy chaotic time series phase space based on variational Bayesian and phase space reconstructive theory.
ARIMA模型可以较准确地预测非稳态随机过程的时间序列。
ARIMA model can be used to predict the non stable time series with adequate precision.
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.
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。
Combining Projection Pursuit(PP) and highdimensional time series analysis, the synthetic earthquake prediction model of highdimensional PP time series is built.
时间序列分析方法是建立变形测量预测模型的主要方法。
Time series analysis method is a main method by which deformation forecasting model is established in deformation measurements.
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。
Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
ARIMA模型；预测；时间序列分析；高血压；发病率。
ARIMA model; Predict; Time series analysis; Hypertension; Incidence.
最后，建立了基于时间序列的二次指数平滑线性预测模型，进行商品销售趋势的分析，部分验证了本文的设计分析。
Finally built a two linear forecasting models based on smoothing of time queue about analysis of sale trend, and verified the design analysis of this paper partly.
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。
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.
用平稳时间序列分析方法建立随机部分模型，并预测沉降随机部分值，二者之和即为某时期沉降预测值。
The random settlement could be gotten by random prediction model that is established by smooth and stable time series analysis method.
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。
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.
最后，以玛纳斯河肯斯瓦特站历年的年径流资料验证时间序列人工神经网络预测模型的可行性与有效性。
Lastly, the feasibility and validity of the model was validated with the past years surface water resource quantity time series data from Kenswat Station on Xinjiang Manas River.
基于时间序列分解法建立大坝变形预测模型。
The forecast model of dam deformation is set up on the basis of the time alignment decomposition method.
基于时间序列分解法建立大坝变形预测模型。
The forecast model of dam deformation is set up on the basis of the time alignment decomposition method.
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