Anomaly Detection Session 3: Auto Regressive Univariate data anomaly detection on Spark/Scala

November 6, 2020
Start Time
9:00 AM PST
End Time
10:00 AM PST


This is part of Machine Learning-Driven Anomaly Detection

What you will learn

Since the data is autoregressive, these algorithms use recent past data to predict anomaly.

Will be discussing

  • Forecast based anomaly detection
  • Decomposition based anomaly detection
  • Dissimilarity based anomaly detection
  • Markov chain based anomaly detection
  • Ngram frequency based anomaly detection
  • Segment based anomaly detection

This is a FREE class!


Can’t make it to live session?  No worries.  Go ahead and register; we will send you the session recording.
See below for past session recordings & notes

Intended Audience

COO, CIO, DevOps, Software Engineers


  • Must have: Development experience
  • Nice to have: Python knowledge

What to Bring

  • Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well;  Personal laptops are recommended)
  • [nice to have] download our docker image elephantscale/es-training
  • Class Notes here

Session Recording


Pranab Ghosh

Pranab Ghosh is a Data Science Consultant, He owns several open-source Big Data and Data Science projects using Hadoop, Spark, Storm, Kafka, NoSQL databases, and the related ecosystem.