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
Knowledge and Information Systems – Springer Journals
Published: Jun 2, 2018
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