Piecewise aggregate approximation method based on cloud model for time series
Piecewise aggregate approximation method based on cloud model for time series
复制标题
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Guo Chong-hui
中科院分区：
文献类型：
--
作者：
Guo Chong-hui
针对时间序列的高维性，提出了一种基于云模型的分段聚集逼近技术。该方法利用云模型的熵来评价子序列中数据点的稳定性，选择稳定性较低的子序列进行进一步划分，得到一系列云模型来逼近时间序列。计算两个云模型序列之间的相似度。该方法具有较好的收敛性和较强的鲁棒性。该方法可以有效地解决时间序列的高维性问题实验结果表明，该方法能够在较大压缩比下保证相似度的准确性，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.