| Trend prediction of chaotic time series |
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| 作者姓名： | 李爱国 赵彩 李战怀 |
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| 作者单位： | [1]School of Computer Science and Engineering, Northwestern Polytechnical University, Xi＇an 710072, China [2]Department of Computer Science and Technology, Xi＇an University of Science and Technology, Xi＇an 710054, China |
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| 摘 要： | Trend prediction of chaotic ti me series is anin-teresting probleminti me series analysis andti me se-ries data mining(TSDM)fields1].TSDM-basedmethods can successfully characterize and predictcomplex,irregular,and chaotic ti me series.Somemethods have been proposed to predict the trend ofchaotic ti me series.In our knowledge,these meth-ods can be classified into t wo categories as follows.The first category is based on the embeddedspace2-3],where rawti me series data is mapped to areconst…
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| 关 键 词： | 混沌 时间序列 趋势预测 数据挖掘 知识获取 |
| 文章编号： | 1671-8267（2007）01-0038-01 |
Trend prediction of chaotic time series |
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| Li Aiguo,Zhao Cai,Li Zhanhuai.Trend prediction of chaotic time series[J].Academic Journal of Xi’an Jiaotong University,2007,19(1):38-41. |
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| Authors: | Li Aiguo Zhao Cai Li Zhanhuai |
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| Abstract: | 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. |
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| Keywords: | knowledge acquisition data mining time series prediction chaos |
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