Matrix Profile Foundation
The Matrix Profile is a recently developed algorithm with the potential to revolutionize time-series analysis. To facilitate community awareness and adoption, we develop and maintain multiple open-source implementations of the Matrix Profile algorithms. All code is hosted on our GitHub repos, and additional support is provided through a dedicated Discord channel, e-mail correspondence and GitHub issue tracking. We strive to simplify technical interactions through logical, standardized APIs across all languages. We also believe that a key value for the Matrix Profile is continued education and expert assistance, particularly for non-technical audiences. We provide interactive resources and tutorials on our website (ui.matrixprofile.org), as well as blog posts and articles containing real-world use cases.
Andrew Van Benschoten
Andrew is the founder and co-developer of matrixprofile-ts. He holds a Ph.D in biophysics from the University of California, San Francisco.
Francisco is the primary developer of the R matrix profile library tsmp. He holds a Masters in Medical Informatics from Universidade do Porto in Portugal.
Tyler is the co-developer of matrixprofile-ts and creator of mass-ts. He is a Data Scientist in the Iowa City, Iowa area holding a Masters in Software Engineering.
Austin is the primary developer for the Golang matrix profile library go-matrixprofile. He holds a BS in Electrical Engineering from University of Illinois - Urbana Champaign.
Matrix Profile algorithms implemented in Python.mass-ts
Mueen's Algorithm For Similary Search (MASS) algorithms implemented in Python.mass-ts-examples
Supporting repository for mass-ts providing examples of usage.
Matrix Profile algorithms implemented in R.
Check It Out!Play around with Matrix Profiles at ui.matrixprofile.org
The Matrix Profile algorithm was pioneered by Professor Eamonn Keogh at the University of California-Riverside and Professor Abdullah Mueen at the University of New Mexico in 2015. Ongoing research has led to breakthroughs in algorithm speed, robustness and use cases across industrial problems. The Matrix Profile Foundation aims to implement new matrix profile discoveries as quickly as possible, in order to maximize impact.