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Improved Symbolic Algorithm of Time Series Based on Statistical Feature Vector

LI Xiaocui,ZHANG Xinyu,LUO Qingyun,REN Chang’an   

  1. (School of Computer and Information Science,Hunan Institute of Technology,Hengyang 421002,China)
  • Received:2014-10-10 Online:2015-10-15 Published:2015-10-15

基于统计特征向量的时序符号化改进算法

李晓翠,张新玉,罗庆云,任长安   

  1. (湖南工学院计算机与信息科学学院,湖南 衡阳 421002)
  • 作者简介:李晓翠(1986-),女,硕士研究生,主研方向:数据挖掘;张新玉,硕士研究生;罗庆云,教授;任长安,讲师。
  • 基金资助:
    湖南省教育厅科学研究基金资助项目(14C0304);国家自然科学基金资助项目(61402164);湖南省科技计划基金资助项目(S2013F1023)。

Abstract: The traditional symbolic algorithm of time series based on statistical feature vector can not retain the timing characteristics well and support multidimensional time series symbolic.Aiming at this problem,this paper proposes an improved symbolic algorithm of time series based on statistical feature vector.The specific methods are as follows:for single-dimensional time series,using special points’ time series segmentation method to segment the time series and making it symbolic;for multi-dimensional time series,using weighted attributes’ Principal Component Analysis(PCA) method to transform the multi-dimensional time series into single time series,then making it symbolic.Experimental result shows that the improved algorithm has higher accuracy than traditional algorithm.It can retain the timing characteristics and has more superiority in the aspect of multidimensional time series symbolization.

Key words: multidimensional time series, feature vector, weighted attribute, symbolic, Principal Component Analysis(PCA)

摘要: 传统基于统计特征向量的时间序列符号化算法不能较好地保留时序数据的特征信息,且不支持多维时间序列的符号化。为此,提出一种改进算法。对于单维时间序列,引入特殊点时间序列分割方法,在其基础上实施符号化。对于多维时间序列,在利用基于加权属性的主成分分析方法将多维时间序列转化为单维时间序列后,再实施符号化。实验结果表明,与传统算法相比,改进算法具有较高的精确度,且能保留时序特征点,同时支持多维时间序列的符号化。

关键词: 多维时间序列, 特征向量, 加权属性, 符号化, 主成分分析

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