Outlier Detection for Time Series Based on Distance and DF-RLS

doi:10.3969/j.issn.1000-3428.2012.12.009

Computer Engineering ›› 2012, Vol. 38 ›› Issue (12): 32-35. doi: 10.3969/j.issn.1000-3428.2012.12.009

• Networks and Communications • Previous Articles Next Articles

Outlier Detection for Time Series Based on Distance and DF-RLS

CHEN Qian ^a,b, HU Gu-yu ^b, LU Wei ^a

(a. Institute of Communication Engineering; b. Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China)

Received:2012-02-14 Online:2012-06-20 Published:2012-06-20

基于距离和DF-RLS的时间序列异常检测

陈乾 ^a,b，胡谷雨^b，路威 ^a

(解放军理工大学 a. 通信工程学院；b. 指挥自动化学院，南京 210007)

作者简介:陈乾(1980－)，男，讲师、博士研究生，主研方向：数据挖掘；胡谷雨，教授、博士、博士生导师；路威，讲师、博士
基金资助:
国家自然科学基金资助项目(61001106)；国家“973”计划基金资助项目(2009CB320400)

Abstract

Abstract: In order to detect both Additive Outliers(AO) and Innovation Outliers(IO) in time series, this paper improves the linear prediction of time series, proposes a Distance Factor Recursive Least Square(DF-RLS) algorithm. It combines DF-RLS with distance-based outlier detection method, proposes a time series outlier detection method based on distance and DF-RLS, named DDR-OD. Experimental results show that the DDR-OD is an effective method for time series outlier detection.

Key words: time series, outlier detection, Recursive Least Square(RLS), distance factor, Additive Outlier(AO), Innovation Outlier(IO)

摘要： 为能同时检测时间序列中的附加异常和革新异常，改进自回归模型，提出距离因子递推最小二乘(DF-RLS)线性预测算法。在此基础上，给出一种基于距离和DF-RLS的联合异常检测方法——DDR-OD。实验结果表明，与当前其他时间序列异常检测方法相比，DDR-OD的检测效果较优。

关键词: 时间序列, 异常检测, 递推最小二乘, 距离因子, 附加异常, 革新异常

CLC Number:

TP311

CHEN Gan, HU Gu-Yu, LU Wei. Outlier Detection for Time Series Based on Distance and DF-RLS[J]. Computer Engineering, 2012, 38(12): 32-35.

陈乾, 胡谷雨, 路威. 基于距离和DF-RLS的时间序列异常检测[J]. 计算机工程, 2012, 38(12): 32-35.

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