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Activity Number: 347 - Machine Learning and Applications in Complex Engineering Systems
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #330888
Title: Hidden in the Signal
Author(s): Eunice Kim* and Ildoo Kim
Companies: Microsoft and Brown University
Keywords: anomaly detection; nonstationary process; spline; nonlinear method

Monitoring processes of physical or administrative systems frequently include unusual and unexpected observations. In a large, expensive experimental setting or in a large throughput data processing center, it is important to identify the anomalies and the associated circumstances. We provide a general solution to flag and adjust anomalies in a regularly-observed process. Depending on the presence of prior information and the distributional characteristics of a process, we vary the required minimum length of the process. In application to a large experimental setting, the final goal is to provide just-in-time anomaly detections and alerts; in an administrative process control setting, a final outcome is to suggest adjustments to correct the surprises.

Authors who are presenting talks have a * after their name.

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