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Anomaly Detection in Time Series ⏲


Hasan Ali
Pulp VIsion, Research Scientist

Hasan is a deep learning researcher and data science coach with 4+ years of experience. While still early in his career, he has worked with Fortune 500 companies, such as Vodafone, Mondelez (Cadbury), Indian Oil, and GAIL. His areas of expertise and research include computer vision, neural network architectures, transformers, and GANs. Hasan specializes in oil and gas, signal processing, document processing, banking, geospatial and satellite imagery, and retail industries and domains.


Workshop Overview:

In enterprises and industries, there is a *ton* of time series and signal data. This workshop is aimed at processing huge chunks of time series and signal data. We will be covering:

  • Analysis of time series or signal data
  • Identifying anomalies and other patterns in the data
  • Different approaches for anomaly and pattern detection
  • Optimal approaches by use case / When to use each approach

Intended Audience:

  • Aspiring Data Scientists
  • Current Data Scientists

Topics Covered:

  • What is an Anomaly?
  • Methods of Anomaly Detection
  • Matrix Profile
  • Practical Use-Cases

Workshop Takeaways:

  • Hands-on Coding Experience
  • Practical Problem Solving in Anomaly Detection
  • Understanding Methods of Anomaly Detection
  • Learning Matrix Profile

To-Do Before Workshop / How to Prepare:

  • Preferably have an anaconda environment (otherwise google collab would do)