基于结构特征的时序聚类方法研究

Time Series Clustering Based on Structural Features

数据驱动的智能运维对提高云平台的管理效率有重要意义。提出一种基于结构特征的时序聚类方法以用于云平台大量性能数据的智能分类。该方法采用分级处理的方式用于降低聚类复杂度,首先基于傅里叶变换将时序分为明显周期型和非明显周期型两大类,然后从时序中提取季节性指标、趋势性指标、偏度、相对熵、样本熵、自相似性和李雅普诺夫系数等7个特征,最后在每个大类中基于特征空间进行K均值聚类分析。实验数据仿真表明:所提方法能够有效将不同波形特性的时序分开。

Data-driven intelligent Operation&Management(O&M)has significant importance for improving the efficiency of cloud platform maintenance.In this paper,a time series clustering method based on structural features is proposed for classifying large-scale metrics in cloud platform.A hierarchical scheme is adopted to reduce the complexity of clustering.First,the time series are classified into two big categories based on Fourier transformation:significant periodicity and non-significant periodicity.Secondly,seven features are extracted from the data:seasonal degree index,trend degree index,skewness,relative entropy,sample entropy,self-similarity and Lyapunov coefficient.And then,the k-means algorithm is used to cluster the time series in the feature space for each big category.The real data experiment shows that the method proposed is able to distinguish the time series which contain different characteristics.