Achieving accurate anomaly detection requires more than statistics. Simple assumptions like normal distribution do not work in the real world. Time series data – representing anything from customer acquisition, to application performance, to manufacturing KPIs – tend to have many different behaviors that need to be modeled accurately. These include seasonal patterns, non-stationary behaviors, and intricate correlations between signals, among others.

Join Ira Cohen, Anodot’s Chief Data Scientist and Bill Vorhies, Editorial Director, Data Science Central, where they will discuss:

  • Fundamental machine learning techniques for anomaly detection
  • Requirements of an anomaly detection system in various use cases
  • Issues and pitfalls to watch out for when implementing anomaly detection
  • Common use cases and examples


Title: Accurate Anomaly Detection with Machine Learning

Date: Thursday, October 13, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

::UPDATE:: Click here to watch the full webinar.

Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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