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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Event Outlier Detection in Continuous Time.

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  • 1Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA.

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This study introduces novel methods for identifying unusual patterns in continuous-time event sequences. These techniques detect unexpected event absences or occurrences, crucial for flagging abnormal situations in real-world data.

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Area of Science:

  • Data Science
  • Statistics
  • Machine Learning

Background:

  • Continuous-time event sequences are common in real-world scenarios.
  • Identifying deviations from normal patterns is vital for anomaly detection.
  • Unexpected events or absences can indicate critical situations requiring attention.

Purpose of the Study:

  • To develop and study methods for detecting outliers in continuous-time event sequences.
  • To address both unexpected event occurrences and unexpected event absences.
  • To incorporate contextual information into outlier detection.

Main Methods:

  • Utilized point processes to model event sequence patterns and context.
  • Applied Bayesian decision theory and hypothesis testing for robust outlier detection.
  • Developed methods with theoretical guarantees for performance.

Main Results:

  • Demonstrated the effectiveness of the proposed outlier detection methods.
  • Validated the approach on both synthetic and real-world clinical event sequence data.
  • Showcased the ability of the methods to identify unexpected events and absences.

Conclusions:

  • The developed point process-based methods accurately detect outliers in continuous-time event sequences.
  • Context-aware outlier detection is crucial for identifying abnormal situations.
  • The methods offer a theoretically sound and empirically validated approach for anomaly detection.