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Trend prediction of chaotic time series

机译:混沌时间序列的趋势预测

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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.
机译:为了在时间序列分析和时间序列数据挖掘领域预测混沌时间序列的趋势,提出了一种新颖的混沌时间序列趋势预测算法,并提出了一种在线分割算法将时间序列转换为二进制字符串。每个子序列的上升或下降趋势。在线分段算法独立于有关时间序列的先验知识。然后采用朴素贝叶斯算法根据二进制字符串来预测混沌时间序列的趋势。三个混沌时间序列的实验结果表明,该方法可以预测混沌时间序列的上升或下降趋势,且误差很小。

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