Knowledge and Information Systems

, Volume 60, Issue 2, pp 1135–1161 | Cite as

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

  • Yan ZhuEmail author
  • Makoto Imamura
  • Daniel Nikovski
  • Eamonn Keogh
Regular Paper


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 robust definitions of time series chains and scalable algorithms that allow us to discover them in massive complex datasets.


Time series Motifs Prognostics Link analysis 



We would like to acknowledge funding from MERL and from NSF IIS-1161997 II and NSF IIS-1510741. We especially want to thank Dr. John Michael Criley and Dr. Gregory Mason for their invaluable advice on the hemodynamics domain, and Dr. Matsubara for providing the GoogleTrend data.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of California, RiversideRiversideUSA
  2. 2.Tokai UniversityTokyoJapan
  3. 3.Mitsubishi Electric Research LaboratoriesCambridgeUSA

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