A Framework for Guided Time Series Motif Discovery
Hoang Anh Dau (University of California, Riverside);Eamonn Keogh (University of California, Riverside)
Abstract
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
