Abstract
The detection of
time series motifs
, which are approximately repeated subsequences in time series streams, has been shown to have great utility as a subroutine in many higher-level data mining algorithms. However, this detection becomes much harder in cases where the motifs of interest are vanishingly rare or when faced with a never-ending stream of data. In this work we investigate algorithms to find such rare motifs. We demonstrate that under reasonable assumptions we must abandon any hope of an exact solution to the motif problem as it is normally defined; however, we introduce algorithms that allow us to solve the underlying problem with high probability.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
40 articles.
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