[1]程艳云,张守超,杨杨. 基于大数据的时间序列异常点检测研究[J].计算机技术与发展,2016,26(05):139-144.
　CHENG Yan-yun,ZHANG Shou-chao,YANG Yang. Research on Time Series Outlier Detection Based on Big Data[J].,2016,26(05):139-144.
点击复制

 基于大数据的时间序列异常点检测研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年05期
页码:
139-144
栏目:
应用开发研究
出版日期:
2016-05-10

文章信息/Info

Title:
 Research on Time Series Outlier Detection Based on Big Data
文章编号:
1673-629X（2016)05-0139-06
作者:
 程艳云; 张守超; 杨杨
 南京邮电大学 自动化学院
Author(s):
 CHENG Yan-yun; ZHANG Shou-chao; YANG Yang
关键词:
 ; 异常点检测; 时间序列; 大数据; 特征提取
Keywords:
 ; outlier detection; time series; big data; feature extraction
分类号:
TN915.07
文献标志码:
A
摘要:
 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.
Abstract:
 According to the detection accuracy and efficiency limitation of traditional time series outlier detection methods when dealing with a large amount of data,a new time 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 deriva-tion 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 show that the big data method has higher precision and rate compared with traditional methods.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(05):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(05):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(05):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(05):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(05):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(05):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(05):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(05):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(05):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(05):47.

更新日期/Last Update: 2016-09-19