Change Detection in Streaming Multivariate Data Using Likelihood Detectors

Abstract: 

Change detection in streaming data relies on a fast estimation of the probability that the data in two consecutive windows come from different distributions. Choosing the criterion is one of the multitudes of questions that need to be addressed when designing a change detection procedure. This paper gives a log-likelihood justification for two well streaming multidimensional data: Kullback T-square test for equal means (H). We propose a semi criterion (SPLL) for change detection. Compared to the existing log change detectors, SPLL tr We examine SPLL together with K real data sets. The criteria were compared using the area under the respective Receiver Operating Characteristic (ROC) curve ( the par with H and better than K both on the normalized data.