Trend prediction of chaotic time series

被引：0

作者：

李爱国 ^{[1
]}

赵彩 ^{[1
]}

李战怀 ^{[2
]}

机构：

[1] Department of Computer Science and Technology,Xi'an University of Science and Technology

[2] School of Computer Science and Engineering,Northwestern Polytechnical University

来源：

Journal of Pharmaceutical Analysis | 2007年 / 01期

关键词：

knowledge acquisition; data mining; time series; prediction; chaos;

D O I：

暂无

中图分类号：

TP311.13 [];

学科分类号：

1201 ;

摘要：

To predict the trend of chaotic time series in time series analysis and time series data mining fields,a novel predicting algorithm of chaotic time series trend is presented,and an on-line segmenting algorithm is proposed to convert a time series into a binary string according to ascending or descending trend of each subsequence.The on-line segmenting algorithm is independent of the prior knowledge about time series.The naive Bayesian algorithm is then employed to predict the trend of chaotic time series according to the binary string.The experimental results of three chaotic time series demonstrate that the proposed method predicts the ascending or descending trend of chaotic time series with few error.

引用

页码：38 / 41

页数：4

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