A new study of two divergence metrics for change detection in data streams Conference Paper uri icon

abstract

  • 2014 The Authors and IOS Press. Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the difference between the data distributions in a current time window and a reference window. Divergence metrics and density estimation are required to measure the difference between the data distributions. Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes due to its asymmetric property and its dependence on the variance of the data. We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area of two distributions. The experimental results show that these two metrics lead to more accurate results in change detection than baseline methods such as Change Finder and using conventional KL-divergence.

published proceedings

  • Frontiers in Artificial Intelligence and Applications

author list (cited authors)

  • Qahtan, A., Wang, S., Carroll, R., & Zhang, X.

complete list of authors

  • Qahtan, A||Wang, S||Carroll, R||Zhang, X

publication date

  • January 2014