Understanding the Behavior of Time Series Data Using the Matrix Profile and Deep Learning
Target is a large retail company with over 1,800 stores in the U.S. Because of this scale, it can be difficult to find anomalous behavior in data or pinpoint what metrics could potentially be correlated. 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.
Frankie Cancino is a Senior Engineer and Data Scientist for Target, a Fortune 50 company, in Minneapolis. While working at Target, he is also a graduate student at the University of Minnesota earning a Master of Science degree in Business Analytics. Frankie is also known as the organizer and founder of the Data Science Minneapolis group. Data Science Minneapolis is a community that brings together professionals, researchers, data scientists, and AI enthusiasts. This community is dedicated to learning, teaching, and building technologies related to data science topics.