Automatic outlier detection for time series: an application to sensor data

被引：184

作者：

Basu, Sabyasachi ^{[1
]}

Meckesheimer, Martin ^{[1
]}

机构：

[1] Boeing Co, Boeing Math Grp, Seattle, WA 98124 USA

来源：

KNOWLEDGE AND INFORMATION SYSTEMS | 2007年 / 11卷 / 02期

关键词：

time series; outliers; jaccard coefficient; simulation; sensor data;

D O I：

10.1007/s10115-006-0026-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

In this article we consider the problem of detecting unusual values or outliers from time series data where the process by which the data are created is difficult to model. The main consideration is the fact that data closer in time are more correlated to each other than those farther apart. We propose two variations of a method that uses the median from a neighborhood of a data point and a threshold value to compare the difference between the median and the observed data value. Both variations of the method are fast and can be used for data streams that occur in quick succession such as sensor data on an airplane.

引用

页码：137 / 154

页数：18