Target is a large retail company with over 1,800 stores. Because of this scale, 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 this data at scale, Target open-sourced the Python library matrixprofile-ts. Using this library, we can layer models on top of the Matrix Profile to find when anomalous behavior occurs or when different metrics in different areas of the company affect each other. This talk will briefly go over the matrixprofile-ts library and examples of where deep learning models can be applied to complement it.
Senior Engineer and Data Scientist for Target, here in Minneapolis. While working at Target, I am currently a grad student at the University of Minnesota earning a Master of Science degree in Business Analytics. I'm also the founder and organizer of Data Science Minneapolis. Passionate about artificial intelligence and building innovative tech for social good.
Does this session sound interesting? You may also like these:
This will add your name to the list of interested participants. It will help us gauge interest for scheduling purposes.