Abstract. Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the differ-.

Oct 22, 2024 · Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes ...

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 ...

We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area ...

A new study of two divergence metrics for change detection in data streams. A Qahtan, S Wang, R Carroll, X Zhang. ECAI 2014, 1081-1082, 2014. 3, 2014. Machine ...

Qahtan, A., Wang, S., Carroll, R., & Zhang, X. (2014). A new study of two divergence metrics for change detection in data streams. Frontiers in Artificial ...

Recall that our basic approach to change detection in data streams uses two sliding windows over the data stream. This reduces the problem of detecting change.

We present a new non-parametric statistic, called the weighed `2 divergence, based on empirical distributions for sequential change detection.

In this paper, we present a novel method for the detection and estimation of change. In addition to providing statisti- cal guarantees on the reliability of ...

May 29, 2024 · This study focuses on detecting drifts in data distributions and divergence within data fields processed from different sample populations.