Knowledge and Information Systems

, Volume 11, Issue 2, pp 137–154

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

Authors

    • The Boeing Math GroupThe Boeing Company
  • Martin Meckesheimer
    • The Boeing Math GroupThe Boeing Company
Regular Paper

DOI: 10.1007/s10115-006-0026-6

Cite this article as:
Basu, S. & Meckesheimer, M. Knowl Inf Syst (2007) 11: 137. doi:10.1007/s10115-006-0026-6

Abstract

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.

Time seriesOutliersJaccard coefficientSimulationSensor data

Copyright information

© Springer-Verlag London Limited 2006