The document discusses using cluster analysis to identify outliers in time series data. Cluster analysis involves grouping similar data points together. It can be used to spot abnormal patterns and outliers in industrial time series data, which is important for tasks like fraud detection, asset health monitoring, and detecting non-technical losses in networks. The presentation provides an example of using Predix Analytics on a non-technical loss detection project, where cluster analysis was used to identify outlier data in time series signals from networks.

1. PAN3: Catching Outliers
with Cluster Analysis
Robin Louvet, GE Energy Connections
robin.louvet@ge.com, @rlouvet

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Agenda
Patterns in Time Series1
Catching Outliers
Cluster Analysis
Predix Analytics
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3
4

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Patterns in Time Series
Time Series is a predominant raw data type in
industry
Signal Processing Independent Single Samples
Machine Learning Huge Volume of Historical Samples

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Catching Outliers
Spotting abnormal patterns can be critical in
industry:
• Fraudulent Transaction Blocking
• Asset Health Monitoring
• Non-technical Losses On Networks

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Cluster Analysis
(source: Wikipedia, Cluster
Analysis)

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Cluster Analysis

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Predix Analytics

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Predix Analytics
{ “ntl-detection-py” : {
"tags": {
"analytic-root": "analytic",
"driver-root": "driver",
"driver-main": "driver/AnalyticDriver.py",
"mapper": "driver",
"resultprovider": "getOutput" } },
"libs": [ "boto3" ],
"conda-libs": [ "numpy", "scipy", "pandas", "scikit-
learn" ] }

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Predix Analytics (demo)

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