sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Comparing Algorithms for Detecting Abrupt Change Points in Data

SESSION: Poster Reception


TIME: 5:15PM - 7:00PM

AUTHOR(S):Cody L. Buntain, Christopher Natoli, Miroslav Živković

ROOM:New Orleans Theater Lobby


Detecting points in data where the underlying distribution changes is not a new task, but much of the existing literature assumes univariate and independent data, assumptions often violated in real data sets. This work addresses this gap in the literature by implementing a set of change point detection algorithms and a test harness for evaluating their performance and relative strengths and weaknesses in multi-variate data of varying dimension and temporal dependence. We then apply our implementations to real-world data taken from structural sensors placed on laboratory a bridge and two years of Bitcoin market data from the Mt. Gox exchange. Though more work is necessary to explore these real-world data sets more thoroughly, our results demonstrate circumstances in which an online, non-parametric algorithm does and does not perform as well as offline, parametric algorithms and provides an early foundation for future investigations.

Chair/Author Details:

Cody L. Buntain - University of Maryland

Christopher Natoli - University of Chicago

Miroslav Živković - University of Amsterdam

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