This paper introduces the feasibility of inner recursion networks using in non-linear ARMA model approaching and time series forecasting .

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。

In this paper modeling and forecasting of time series and optimal estimation algorithm are discussed the optimal estimation method and its improved algorithms are applied to time series forecasting .

本文对时间序列建模与预测和最优估计算法进行研究，将最优估计方法及其改进算法应用于 时间 序列 预测 领域，通过最优估计思想优化其 时间序列模型参数，达到提高 时间 序列 预测 精度的目的。

On runoff time series forecasting this method shows a good result too . 3 .

研究还表明，该方法在水文月径流 时间 序列 的 预测中同样有效。

Some problems in applications of neural network methods to financial time series forecasting are introduced . Same methods to solve these problems are proposed .

概述了神经网络方法在金融 时间 序列 预测应用中所面临的有关问题，给出了解决方法；

This paper defines the idea of consistence of sample information and proposes an empirical formula on selection of embedded dimension of time series . It then constructs a modal of feed-forward neural net for time series forecasting .

提出了 时间 序列 预测问题中样本相关信息一致性的想法和一个时间序列嵌入维数选择的经验公式，并据此给出 时间 序列 预测的一个前馈神经网络模型。

In this dissertation several modern analysis technologies such as artificial neural network wavelet analysis support vector machine self-memory model and chaotic theory were applied to hydrologic time series forecasting respectively .

本文以神经网络、小波分析、支持向量机、自记忆模型和混沌理论这几种现代分析技术为 预测手段进行了水文 时间 序列的预测研究。

Wavelet based on the integration of chaos and chaotic time series forecasting methods is also put forward .

提出了基于小波与混沌集成的混沌 时间 序列 的 预测方法。

Based on a single time dimension time series forecasting methods are difficult to solve the traffic flow a high degree of complexity randomness and uncertainties of the predicted effect is not satisfactory .

基于单一时间维度 时间 序列 的 预测方法难以解决短时交通流高度复杂性、随机性和不确定性的问题，预测效果并不令人满意。

Time Series Forecasting Based on Variable-window Neural Networks Ensemble

基于变窗口神经网络集成的 时间 序列 预测

In this paper the main experimental Shanghai Composite Index for the week of the closing price and time series forecasting models and support vector machine algorithm to predict .

本文的实验主要针对上海综合指数的周收盘价，采用 时间 序列 预测模型和支持向量机算法进行预测。

This paper studies the choice of chaotic time series phase space reconstruction parameters and chaotic time series forecasting methods .

本文研究了混沌时间序列相空间重构参数的选取和混沌 时间 序列 的 预测方法。

And ultimately support vector regression and time series forecasting models combining we can get more accurate forecasts .

并最终将支持向量回归机和 时间 序列模型的 预测结合起来，就可以得到更为准确的预测值。

In order to improve the prediction accuracy of iron ore consumption using a time series forecasting method based on intelligent calculation .

为了提高铁矿石消费量的预测精度，采用一种基于智能计算的 时间 序列 预测方法。

Time Series Forecasting and Its Application in Telecommunications Prediction

时间 序列 预测新 方法及其在电信预测中的应用

Research on Chaotic Time Series Forecasting and Chaotic Optimization

混沌 预测与混沌优化理论与算法研究

Load time series forecasting based on additive fuzzy systems

基于可加性模糊系统的负荷 时间 序列 预测

Based on achievement of comprehensive time series forecasting and neural network prediction the paper proposed price forecasting model of mixed time series based on support vector machines .

通过综合 时序 预测和神经网络预测的研究成果，提出基于改进支持向量机的时序混合电价预测模型。

The study methods this paper proposes can also be applied in others financial securities time series forecasting .

本文提出的研究方法亦可应用于其他金融证券 时间 序列 预测中。

The Research on Environmental Time Series Forecasting Method Based on Soft Computation Technique

基于软计算技术的环境 时序 预测方法研究

Influence analysis and self-adaptive optimization of support vector machine time series forecasting model parameters

支持向量机 时间 序列 预测模型的参数影响分析与自适应优化

Grey system model and Markov chain can be applied to the time series forecasting but their own characteristics and limitations .

灰色系统模型与马尔科夫链都可以应用于 时间 序列 预测，但它们各具特色和局限。

Gaussian Wavelet SVM and Its Applications to Chaotic Time Series Forecasting

Gaussian小波SVM及其混沌 时间 序列 预测

Furthermore general methods and procedure for economic time series forecasting models are proposed based on continuous parameter wavelet networks which are used in forecast simulation of the time series for the import-export trade in China .

进一步，通过对中国进出口贸易额时间序列预测建模的研究和仿真预测，提出了用连续参数小波网络建立经济 时间 序列 预测模型的一般步骤和方法。

With the development of chaotic theory nonlinear time series forecasting has been widely applied in many areas .

随着非线性时间序列分析的发展，基于非线性 时间 序列 的 预测在很多领域中的应用越来越重要。

This paper presents a nonlinear time series forecasting model based on recurrent composite BP networks It also establishes a multivariable time series model is aimed at concrete examples .

提供了一种基于递推合成BP网络的非线性 时间 序列 预测方法，并针对具体实例建立多变量时间序列模型。

Support vector regression based on grey system for time series forecasting

基于灰色系统的支持向量回归 预测 方法

Support Vector Machine Applied to Air Pollutant Time Series Forecasting

支持向量机应用于大气污染物 时间 序列 预测

Establishment and application of hydrological time series forecasting model based on KPCA_SVM

KPCASVM水文 时间 序列 预测模型的建立与应用

The history of artificial neural network is nearly half a century . In recent years the research of Chinese artificial neural network has made many achievements . This paper studies neural networks with focus on nonlinear time series forecasting application .

人工神经网络的研究已有近半个世纪的历史，在最近几年里，我国在人工神经网络方面的研究取得了不少成果，本文主要研究神经网络在非线性 时间 序列 预测方面的应用。

The chaotic time series forecasting method based on RBF neural network are proposed . The given power load time series are the power load records of a city in China .

最后，针对电力负荷时间序列的 预测，本文对几种混沌时间序列预测方法的理论分析和 对比，提出了选用基于RBF神经网络的混沌 时间 序列 预测法。