Precipitation Time Series Predicting of the Chaotic Characters Using Support Vector Machines

被引：4
|
作者：
Li Haitao [1 ]
Zhang Xiaofu [1 ]
机构：
[1] Anyang Normal Univ, Sch Civil Engn & Architecture, Anyang, Henan, South Korea
来源：
2009 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT, INNOVATION MANAGEMENT AND INDUSTRIAL ENGINEERING, VOL 3, PROCEEDINGS | 2009年
关键词：
chaotic time series; phase space reconstruction; pecipitation; support vector machines;
D O I：
10.1109/ICIII.2009.407
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Based on the powerful nonlinear mapping ability of support vector machines, the predicting model of support vector machines in combination with Takens' delay coordinate phase reconstruction of chaotic time series has been established. Yearly precipitation time series is of the chaotic characters, thus this model is used to try predicting the precipitation. Because of the peculiarity of precipitation time series, the mean-square-error is used as the criterion to choose the embedding dimension and model parameters. Case study proved the precise of this model to predicting the precipitation. Besides, this result also shows that support vector machines is one of tools to study chaotic time series.
引用
收藏
页码：407 / 410
页数：4
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