time series vector

And then introduced three single forecasting methods used in railway passenger volume prediction: multiple regression analysis prediction, time series prediction, support vector machine regression analysis.

接着介绍了应用于铁路客运量预测的三种单一预测方法：多元线性回归分析预测法、时间序列分析预测法以及支持向量机回归分析预测法。

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Predicting Chaotic Time Series Using Support Vector Machines Optimized by Genetic Algorithm

GA优化支持向量机用于混沌时间序列预测

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Prediction of gas concentration chaotic time series based on support vector machines

基于SVM的瓦斯体积分数混沌时间序列预测

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The simulation result indicates that carries on the chaos time series with the support vector return algorithm the forecast can obtain better effects compared to other method even, has the very good steadiness and exudes the ability.

仿真结果表明，用支持向量回归算法进行混沌时间序列的预测能够取得比其他方法更好的效果，具有很好的稳健性及泛化能力。

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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。

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The Prediction of Financial Time Series Based on Support Vector Machine

基于支持向量机的金融时间序列预测

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Study on Wind Speed Forecasting of Wind Farm Based on Time Series and Support Vector Machine

基于时间序列与支持向量机的风电场风速预测研究

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To detect scanning network worm attack, failed connection flow ( FCT) time series is established based on characteristics of worm attack, and a novel approach for worm detection based on energy features of FCT time series and support vector machine ( SVM) is proposed.

根据网络蠕虫攻击的特点，建立了能够反映蠕虫扫描特征的失败连接流量（FCT）时间序列，提出了一种基于FCT时间序列小波包能量特征和支持向量机（SVM）的蠕虫检测新方法。

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Research on Time Series Based on Support Vector Regression Model for Deformation Observation

基于时间序列支持向量回归模型的变形监测研究

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Analyze the application of kernel function in SVM including support vector regression ( SVR), kernel time series prediction and kernel principal component analysis ( KPCA) comprehensive evaluation model.

就SVM的核方法在经济预警系统中的应用进行了分析，包括支持向量回归、支持向量时间序列预测方法和核主成分分析（KPCA）多指标综合评价方法，并给出了数据实验。

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The Classification of Time Series Employing Feature Vector and Binary Tree

时间序列基于特征向量的分类与二叉树分类

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This paper applies Least Squares Support Vector Machine ( LS-SVM) to chaotic time series prediction, and compares the prediction results with BP network and RBF network.

文中将最小二乘支持向量机算法应用于混沌时间序列预测中，并同BP网络及RBF网络的预测结果进行了比较分析。

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