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Activity Number:
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509
- New Approaches to Modeling and Inference for Complex Space-Time Data
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #329998
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Title:
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Automatic Anomaly Detection in Modeling Real-Time Sensor Data
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Author(s):
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Bei Chen* and Beat Buesser
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Companies:
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IBM Research and IBM Research
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Keywords:
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outlier;
anomaly detection ;
time series ;
IOT;
sensor data
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Abstract:
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Time series collected by sensors are usually massive and noisy. One of the important steps to model such data is data cleansing. In this talk I will present two novel algorithms for anomaly detection: iterative GAM filtering and parallel recursive multi-agent search. To further improve the accuracy and flexibility, I will introduce a voting scheme for combining the results of the two algorithms. The proposed methods will be demonstrated by an example of modeling data from the sensor-equipped buildings, including energy/gas consumption, Co2 emission and occupancy.
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Authors who are presenting talks have a * after their name.
