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Activity Number:
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280
- Leading the Stream: Novel Methods for Streaming Data
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #326919
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Title:
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Efficient Detection of Anomalies Within Streaming Data
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Author(s):
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Alexander Fisch and Idris Eckley* and Paul Fearnhead
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Companies:
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Lancaster University and Lancaster University and Lancaster University
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Keywords:
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anomaly detection;
changepoint;
data stream;
dynamic programming
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Abstract:
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The ubiquity of sensors in everyday systems and devices is resulting in a torrent of data. Such data pose fundamentally new and exciting questions for Statistics. Amongst the various challenges arising, arguably one of the most important is the detection of anomalous structure. This talk will introduce recent work in this area, describing an exact and computationally efficient approach to detect anomalies in data streams and its application to data provided by an industrial collaborator.
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Authors who are presenting talks have a * after their name.
