Sean Law - Modern Time Series Analysis with STUMPY - Intro To Matrix Profiles | PyData Global 2020

Traditional time series analysis techniques have found success in a variety of data mining tasks. However, they often require years of experience to master and the recent development of straightforward, easy-to-use analysis tools has been lacking. STUMPY is a scientific Python library for modern time series analysis that efficiently computes something called a matrix profile and leverages popular open source software and enables you to do better science! https://github.com/TDAmeritrade/stumpy https://stumpy.readthedocs.io Sean Law is a senior applied scientific researcher and lead data scientist currently working with a multi-talented Exploration Lab team and serves as an advisor on an enterprise A.I. Council at TD Ameritrade. He has experience producing cutting edge methodologies, building high-performance predictive models, and developing rapid prototypes. Additionally, he is one of the co-organizers of PyData Ann Arbor and is also the creator and core maintainer of STUMPY, a powerful and scalable open source Python library that can be used for a variety of time series data mining tasks. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps