TY - GEN
T1 - Matrix profile XIII
T2 - 9th IEEE International Conference on Big Knowledge, ICBK 2018
AU - Imani, Shima
AU - Madrid, Frank
AU - Ding, Wei
AU - Crouter, Scott
AU - Keogh, Eamonn
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/12/24
Y1 - 2018/12/24
N2 - Perhaps the most basic query made by a data analyst confronting a new data source is “Show me some representative/typical data.” Answering this question is trivial in many domains, but surprisingly, it is very difficult in large time series datasets. The major difficulty is not time or space complexity, but defining what it means to be representative data in this domain. In this work, we show that the obvious candidate definitions: motifs, shapelets, cluster centers, random samples etc., are all poor choices. Thus motivated, we introduce time series snippets, a novel representation of typical time series subsequences. Beyond their utility for visualizing and summarizing massive time series collections, we show that time series snippets have utility for high-level comparison of large time series collections.
AB - Perhaps the most basic query made by a data analyst confronting a new data source is “Show me some representative/typical data.” Answering this question is trivial in many domains, but surprisingly, it is very difficult in large time series datasets. The major difficulty is not time or space complexity, but defining what it means to be representative data in this domain. In this work, we show that the obvious candidate definitions: motifs, shapelets, cluster centers, random samples etc., are all poor choices. Thus motivated, we introduce time series snippets, a novel representation of typical time series subsequences. Beyond their utility for visualizing and summarizing massive time series collections, we show that time series snippets have utility for high-level comparison of large time series collections.
KW - Diversification
KW - Motifs
KW - Sampling
KW - Time series
UR - https://www.scopus.com/pages/publications/85061389849
UR - https://www.scopus.com/pages/publications/85061389849#tab=citedBy
U2 - 10.1109/ICBK.2018.00058
DO - 10.1109/ICBK.2018.00058
M3 - Conference contribution
AN - SCOPUS:85061389849
T3 - Proceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018
SP - 382
EP - 389
BT - Proceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018
A2 - Soon, Ong Yew
A2 - Chen, Huanhuan
A2 - Wu, Xindong
A2 - Aggarwal, Charu
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 November 2018 through 18 November 2018
ER -