Understanding the Behavior of Time Series Data Using the Matrix Profile

It can be difficult to find anomalous behavior in data or pinpoint what metrics could potentially be related.

In order to understand the behavior of data at scale, there is the open-sourced Python library - matrixprofile (https://github.com/matrix-profile-foundation/matrixprofile).

Using this library, we can use the Matrix Profile to find when anomalous behavior occurs or when different metrics in different areas of the company seemingly affect each other.

This talk will briefly go over the matrixprofile library and examples of where it can be applied.

Our speaker – Frankie Cancino, Senior AI Scientist at Target AI. He focuses on modernizing supply chain.

Frankie holds a Master’s degree from the University of Minnesota and is the organizer and founder of "Data Science Minneapolis”, a community that brings together professionals, researchers, data scientists, and AI enthusiasts.

Join the Bugout Slack dev community to connect with fellow data scientists, ML practitioners, and engineers: https://join.slack.com/t/bugout-dev/shared_invite/zt-fhepyt87-5XcJLy0iu702SO_hMFKNhQ