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Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Graph-based anomaly detection: When and how

Jeffrey Yau (Silicon Valley Data Science)
2:40pm3:20pm Thursday, March 16, 2017
Real-time applications
Location: 210 A/E Level: Advanced
Secondary topics:  Financial services, Hardcore Data Science, IoT, Streaming
Average rating: ***..
(3.20, 5 ratings)

Who is this presentation for?

  • Data scientists

Prerequisite knowledge

  • Familiarity with time series models and Spark

What you'll learn

  • Understand the key characteristics of graph-based, large-scale time series mining and traditional time series techniques, as well as their theoretical appeal when applied to anomaly detection
  • Explore these techniques through examples from credit card fraud, wearable IoT devices, and financial time series

Description

Thanks to frameworks such as Spark’s GraphX and GraphFrames, graph-based techniques are increasingly applicable to anomaly, outlier, and event detection in time series. However, most data do not naturally come in the form of a network that can be represented in graphs. Therefore, it is not clear whether graph-based techniques always offer the most appropriate approach to detect anomalies.

Jeffrey Yau offers an overview of applying graph-based techniques and outlines the benefits of graphs relative to other techniques. Jeffrey compares and contrasts the use of graph theory and techniques, large-scale time series mining methods, and traditional parametric linear and nonlinear time series techniques in anomaly, outlier, and event detection—with specific examples from credit card fraud, wearable IoT devices, and financial time series.

Topics include:

  • Static graphs
  • Dynamic graphs
  • The most common large-scale time series mining methods
  • Traditional parametric linear and nonlinear time series techniques, including change-point detection
  • Trade-offs need to be made when applying each of these classes of techniques to identify anomalies
Photo of Jeffrey Yau

Jeffrey Yau

Silicon Valley Data Science

An expert in quantitative modeling with a strong background in finance, Jeffrey Yau has over 17 years of experience applying econometric, statistic, and mathematical modeling techniques to real-world challenges. As vice president of data science at Silicon Valley Data Science, Jeffrey has a passion for leading data science teams in finding innovative solutions to challenging business problems.