Abstract
In this work we present DRUM, an unsupervised approach that is based on statistical properties of multivariate data streams to identify regime shifts in real time. DRUM processes streams in small chunks, learns their statistical properties, and makes generalizations as time goes by. We show how this straightforward approach requires minimal computation and reaches state of the art accuracy, making it ideal for embedded and cyber physical systems.
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This work was supported by National Science Foundation (NSF) EPSCoR grant number OIA-1757207.
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Bashir, A., Estrada, T. (2023). DRUM: A Real Time Detector for Regime Shifts in Data Streams via an Unsupervised, Multivariate Framework. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_27
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