Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery

author: Hoang Anh Dau, Department of Computer Science and Engineering, University of California, Riverside
published: Oct. 9, 2017,   recorded: August 2017,   views: 1042

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Time series motif discovery has emerged as perhaps the most used primitive for time series data mining, and has seen applications to domains as diverse as robotics, medicine and climatology. There has been recent significant progress on the scalability of motif discovery. However, we believe that the current definitions of motif discovery are limited, and can create a mismatch between the user's intent/expectations, and the motif discovery search outcomes. In this work, we explain the reasons behind these issues, and introduce a novel and general framework to address them. Our ideas can be used with current state-of-the-art algorithms with virtually no time or space overhead, and are fast enough to allow real-time interaction and hypotheses testing on massive datasets. We demonstrate the utility of our ideas on domains as diverse as seismology and epileptic seizure monitoring.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: