基于云模型的时间序列分段聚合近似方法

Piecewise aggregate approximation method based on cloud model for time series

针对时间序列数据的高维特性,提出一种基于云模型的时间序列分段聚合近似方法.利用云模型的熵评判分段聚合后各子序列的数据稳定性,选取稳定性最弱的子序列再分段聚合,最终得到云模型序列,同时给出了云模型序列的相似性度量.该方法对时间序列能够有效降维,并能够自适应地识别和描述其基本特征.实验结果表明,数据压缩较大时,所提出方法能够较好地保证近似的准确性,并提高时间序列数据挖掘的效率.

This paper proposes a technique of piecewise aggregate approximation based on cloud model to resolve the high dimensionality of time series.The entropy of cloud model is used to evaluate the stability of data points in a subsequence and choose the subsequence with lower stability to further divide so that a series of cloud models can be obtained to approximate time series.The similarity between two cloud model series is calculated.The proposed method can reduce the dimensionality,and also can adaptively recognize and represent the essential features of time series.The results of experiments indicate that the proposed method can guarantee the accuracy of similarity and improve the efficiency of time series data mining under larger compress ratio.