Finding Motifs in Time Series

By Jessica Lin. Knowledge Discovery and Data Mining, 2002

549 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d04d4b5e42c51711e4213eab40578ed96ded0ccc

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Citations in the corpus

Experiencing SAX: a novel symbolic representation of time series (2007, background): “• Summarization: Given a time series Q containing n datapoints where n is an extremely large number, create a (possibly graphic) approximation of Q which retains its essential features but ﬁts on a single page, computer screen, executive summary etc (Lin et al. 2002).”
A review on time series data mining (2011, background): “In addition, Chiu et al. (2003) address the limitations discussed in Lin et al. (2002) on poor scalability of the motif discovery algorithm and the inability to discover motifs in the presence of noise by applying a probabilistic model to the algorithm.”
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling (2020, methodology): “For time series, we can create a large set of LFs based on motifs (Lonardi & Patel, 2002) or graphs of temporal constraints (GuillameBert & Dubrawski, 2017).”
Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns (2015, unknown): “These patterns are often called ‘motifs’ [12, 6].”
A symbolic representation of time series, with implications for streaming algorithms (2003, methodology): “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms Jessica Lin Eamonn Keogh Stefano Lonardi Bill Chiu University of California -Riverside Computer Science & Engineering Department Riverside, CA 92521, USA {jessica, eamonn, stelo, bill}@cs.ucr.edu ABSTRACT The…”