Signature-Based Detection of Notable Transitions in Numeric Data Streams

A major challenge in large-scale process monitoring is to recognize significant transitions in the process conditions and to distinguish them from random fluctuations that do not produce a notable change in the process dynamics. Such transitions should be recognized at the early stages of their development using a minimal "snapshot" of the observable process log. We developed a novel approach to detect notable transitions based on analysis of coherent behavior of frequency components in the process log (coherency portraits). We have found that notable transitions in the process dynamics are characterized by unique coherency portraits, which are also invariant with respect to random process fluctuations. Our experimental study demonstrates significant efficiency of our approach as compared to traditional change detection techniques.