基于大数据的时间序列异常点检测研究

Research on Time Series Outlier Detection Based on Big Data

针对传统时间序列异常点检测方法在处理大量数据时检测精度与效率低下的缺陷,文中提出一种基于大数据技术的全新时间序列异常点检测方法。首先介绍了传统时间序列异常点检测方法并分析了其缺陷。其次介绍了基于大数据方法的理论推导,包括特征提取、奇异点检测及异常点判别,具体为采用大数据方法将海量序列分解为周期分量、趋势分量、随机误差分量及突发分量四个不同分量,对不同分量进行特征提取并根据特征提取结果进行奇异点检测,并在此基础上利用序列特点判别奇异点是否为异常点。最后通过实验分析对比验证大数据方法的可行性与效率。实验结果表明,基于大数据方法的时间序列异常点检测相比于传统的方法具有更高的检测精度与更快的检测速率。

According to the detection accuracy and efficiency limitation of traditional time series outlier detection methods when dealing with a large amount of data,a newtime series outlier detection method is put forward,which is based on the big data technology. Firstly,the traditional time series outlier detection methods are introduced,analysis of their defects. Secondly,it introduces the theoretical derivation of big data method in this paper,which can be divided into feature extraction,abnormal detection and outlier distinguish. The massive series is decomposed into four different components,including periodic component,trend component,random error component and burst component. Then the feature is extracted to four components and abnormal detection is made according to the result of extraction. On this basis it determines whether abnormal point is outlier by series characteristic. Finally,the feasibility and efficiency of big data approach is verified by experiment analysis and comparison. The results showthat the big data method has higher precision and rate compared with traditional methods.