Scalable Online Change Detection for High-dimensional Data Streams. (arXiv:2205.12706v1 [cs.LG])

Detecting changes in data streams is a core objective in their analysis and
has applications in, say, predictive maintenance, fraud detection, and
medicine. A principled approach to detect changes is to compare distributions
observed within the stream to each other. However, data streams often are
high-dimensional, and changes can be complex, e.g., only manifest themselves in
higher moments. The streaming setting also imposes heavy memory and computation
restrictions. We propose an algorithm, Maximum Mean Discrepancy Adaptive
Windowing (MMDAW), which leverages …

arxiv change data data streams detection scalable