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20182020
most citedTime series classification for varying length series

19 citations · 22 across the 4 of their papers we have counts for

collaborators

8 papers

cs.LG2020

Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW

Sara Alaee, Kaveh Kamgar, Eamonn Keogh

Over the last decade, time series motif discovery has emerged as a useful primitive for many downstream analytical tasks, including clustering, classification, rule discovery, segm…

cs.DB20202 cited

Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series

Michele Linardi, Yan Zhu, Themis Palpanas +1

In the last fifteen years, data series motif and discord discovery have emerged as two useful and well-used primitives for data series mining, with applications to many domains, in…

cs.DB2020

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series

Michele Linardi, Yan Zhu, Themis Palpanas +1

Data series motif discovery represents one of the most useful primitives for data series mining, with applications to many domains, such as robotics, entomology, seismology, medici…

cs.LG20191 cited

Features or Shape? Tackling the False Dichotomy of Time Series Classification

Sara Alaee, Alireza Abdoli, Christian Shelton +3

Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have u…

cs.LG2019

Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior

Alireza Abdoli, Amy C. Murillo, Alec C. Gerry +1

Poultry farms are a major contributor to the human food chain. However, around the world, there have been growing concerns about the quality of life for the livestock in poultry fa…

cs.LG201919 cited

Time series classification for varying length series

Chang Wei Tan, Francois Petitjean, Eamonn Keogh +1

Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths.…