The grey system has a high precision for the time series prediction.
灰色系统预测法对时间序列的预测有较高的精度。
Black body furnace temperature time series prediction model based on BPNN was built.
文章在神经网络的基础上，建立了黑体炉温度时序预测模型。
A routing algorithm based on time series prediction for delay tolerant network is proposed.
本文提出了一种基于时间序列预测的延迟容忍网络路由算法。
A method of chaotic time series prediction problem based on local dynamical similarity is proposed.
基于混沌系统局部特征，提出了一种局部动力相似的混沌时间序列的预测方法。
Next, the stock price prediction model is proposed on the base of nonlinear time series prediction theory.
根据部分可量化股价影响因素，选取预测模型的输入变量。
Stock market price prediction is regarded as a challenging task of the financial time series prediction process.
股票市场价格预测一直以来都被认为是金融时序预测领域的一项具有挑战性的工作。
Time series prediction is an application domain of neural network. Most studies on it are focused on direct prediction.
时间序列预报是神经网络的一个应用领域，多数研究集中在神经网络直接预报方面。
In this paper, the theory of gray system is adopted to study the time series prediction of the leaching rate of in-situ blasting and leaching ore.
采用灰色系统理论对原地爆破浸出率的时间序列预测问题进行了研究。
As a new type of recurrent neural network, echo state network (ESN) is applied to nonlinear system identification and chaotic time series prediction.
ESN(回声状态网络)是一种新型的递归神经网络，可有效处理非线性系统辨识以及混沌时间序列预测问题。
The simulation results from function fitting and time series prediction indicate that UGEP performs better than other similar algorithms in each of experimental.
试验结果也证明，在求解函数拟合和时间序列预测等实际问题时，对比同类算法，UGEP算法体现出了较大的优越性。
The performances of some classic time series prediction models were analyzed together concerning the traffic prediction of General Packets Radio Service (GPRS) cells.
针对通用无线分组业务(GPRS)小区流量预测问题，对几种典型时序预测模型的性能进行了综合分析。
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。
The time series analysis can also be used in ship pitching and heaving time series prediction. These indicate that the prediction method is valuable for engineering practice.
时间序列分析法亦可用于船舶纵摇、艏摇的时间序列预报，该方法在工程中具有很大的实用价值。
In our daily life, there are various kinds of time series data, and time series prediction becomes one of the important aspects of data Mining and Knowledge Discovery (DMKD).
在日常生活中广泛存在着各种时间序列数据，发现时间序列知识、对时间序列进行预测正成为数据挖掘与知识发现的重要内容。
Wavelet network based nonlinear time series prediction model is submitted, and nonlinear time series prediction and its application in fault prediction are discussed in this paper.
本文提出了基于小波网络的非线性时间序列预报模型，探讨了非线性时间序列预报在故障预报中的应用。
Combining wavelet analysis and neural network characteristics, the error back propagation wavelet neural network based structure and algorithm to ship roll time series prediction are given.
结合小波分析和神经网络的特点，建立了应用于船舶横摇运动时间序列预报的误差反传小波神经网络结构并给出了算法。
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.
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。
SVM applications, such as pattern recognition, function approaching, time series prediction, fault prediction and recognition, information security, power system and power electronics, are described.
归纳了支持向量机在诸如模式识别、函数逼近、时间序列预测、故障预测和识别、信息安全、电力系统以及电力电子领域中的应用。
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。
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.
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。
By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series.
这一概念是对线性偏自相关的一般化，由它可以得到度量时间序列预测复杂性的定量方法。
The concept is the generalization of partial autocorrelation. By means of it, we could get the quantitative method to measure the intrinsic prediction complexity of time series.
这一概念是对线性偏自相关的一般化，由它可以得到度量时间序列预测复杂性的定量方法。
A new multi-branch time delay neural network is adopted to conduct prediction research on chaotic time series.
采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究。
The prediction of time series is very important in the economic and engineering fields.
时间序列的预测在经济和工程领域具有十分重要的意义。
Because stock forecasting is a uncertain, nonlinear and nonstationary time series problem, it is difficult to achieve a satisfying prediction effect by traditional methods.
由于股票预测是不确定、非线性、非平稳的时间序列问题，传统的方法往往难以取得满意的预测效果。
How to identify chaos is the foundation of analysis, prediction and control of nonlinear time series.
识别混沌是对非线性时间序列进行分析、预测、控制的基础。
The prediction method of weight local basis function is presented based on the deep research on local prediction for chaotic time series.
在深入研究混沌时间序列局域预测方法的基础上，提出了一种加权局域基函数预测方法。
The prediction error correction is based on time series analysis, parameters estimation and optimum prediction principle.
预报误差校正是基于时间序列分析、参数估计和最优预报原理形成的。
During this course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction.
课程期间我们将检视数种学习技巧在一些领域上的应用如电脑视觉、电脑绘图、数据库搜索和时间数列分析与预测。
During this course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction.
课程期间我们将检视数种学习技巧在一些领域上的应用如电脑视觉、电脑绘图、数据库搜索和时间数列分析与预测。
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