一种基于信息熵的时间序列分段线性表示方法

Piecewise linear representation based on information entropy of time series

针对部分时间序列具有高维、大数据量及数据更新速度较快的特点,导致在原始时间序列上难以进行数据挖掘的问题,提出一种基于信息熵的时间序列分段线性表示方法———PLR_IE。该算法利用信息熵作为评判重要点数量的性能指标,从序列中提取重要分段点的数量分布情况,利用重要点组成的序列重新拟合原始时间序列,为下一步数据挖掘提供基础。实验结果表明,该方法能高效地提取出序列主要特征、拟合原始序列。

For some time series with high dimension, large amount of data and data renewal speed characteristics, resulting in the original time series data mining on difficult problems, this paper presented a method piecewise linear representation of time series method based on information entropy, which was the piecewise linear representation of time series algorithm PLR_IE. The algorithm used the information entropy as an evaluation of important points of performance indicators, from sequence to ex- tract important segment, the important point consisted of a sequence of fitting the original time series. Experiments show that this method can efficiently extract sequence, main characteristics, fitting the original sequence.