Introducing time series chains: a new primitive for time series data mining

Introducing time series chains: a new primitive for time series data mining Knowl Inf Syst https://doi.org/10.1007/s10115-018-1224-8 REGULAR PAPER Introducing time series chains: a new primitive for time series data mining 1 2 3 Yan Zhu · Makoto Imamura · Daniel Nikovski · Eamonn Keogh Received: 22 December 2017 / Accepted: 21 May 2018 © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Time series motifs were introduced in 2002 and have since become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work, we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns can be arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain “data, date, cate, cade, code” from text stream. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce two http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Introducing time series chains: a new primitive for time series data mining

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag London Ltd., part of Springer Nature
Subject
Computer Science; Information Systems and Communication Service; Database Management; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Information Systems Applications (incl.Internet); IT in Business
ISSN
0219-1377
eISSN
0219-3116
D.O.I.
10.1007/s10115-018-1224-8
Publisher site
See Article on Publisher Site

Abstract

Knowl Inf Syst https://doi.org/10.1007/s10115-018-1224-8 REGULAR PAPER Introducing time series chains: a new primitive for time series data mining 1 2 3 Yan Zhu · Makoto Imamura · Daniel Nikovski · Eamonn Keogh Received: 22 December 2017 / Accepted: 21 May 2018 © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Time series motifs were introduced in 2002 and have since become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work, we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns can be arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain “data, date, cate, cade, code” from text stream. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce two

Journal

Knowledge and Information SystemsSpringer Journals

Published: Jun 2, 2018

References

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