Particle swarm optimization for time series motif discovery

@article{Serr2016ParticleSO,
  title={Particle swarm optimization for time series motif discovery},
  author={Joan Serr{\`a} and Josep Llu{\'i}s Arcos},
  journal={Knowl. Based Syst.},
  year={2016},
  volume={92},
  pages={127-137}
}

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References

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Constrained Motif Discovery in Time Series
TLDR
The Constrained Motif Discovery problem is defined which enables utilization of domain knowledge into the motif discovery process and two algorithms called MCFull and MCInc are provided for efficiently solving the constrained motif discovery problem.
Exact Discovery of Time Series Motifs
TLDR
For the first time, a tractable exact algorithm to find time series motifs is shown and it is shown that this algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization.
Efficient Proper Length Time Series Motif Discovery
TLDR
This work proposes a novel algorithm using compression ratio as a heuristic to discover meaningful motifs in proper lengths using time series motifs as a hypothesis and demonstrates that the proposed method outperforms existing works in various domains both in terms of speed and accuracy.
Finding Motifs in Time Series
TLDR
An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases and could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification.
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TLDR
This work fully exploit state of the art iSAX representation multiresolution capability to obtain motifs at different resolutions and yields interactivity, allowing the user to navigate along the Top-K motifs structure, allowing a deeper understanding of the time series database.
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TLDR
This work introduces a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences, which is probabilistic in nature, but can find time series motifs with very high probability even in the presence of noise or "don't care" symbols.
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TLDR
This work introduces a new algorithm that allows discovery of time series motifs with invariance to uniform scaling, and shows that it produces objectively superior results in several important domains.
Approximate variable-length time series motif discovery using grammar inference
TLDR
A novel approach, based on grammar induction, is proposed for approximate variable-length time series motif discovery, which offers the advantage of discovering hierarchical structure, regularity and grammar from the data.
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TLDR
This paper argues that it is not trivial to extend this MK algorithm to handle multiple motifs of variable lengths when constraints of maximum overlap are to be satisfied which is the case in many real world applications and proposes an extension of the MK algorithm called MK++ to handle these conditions.
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TLDR
This paper develops the first online motif discovery algorithm which monitors and maintains motifs exactly in real time over the most recent history of a stream and allows useful extensions of the algorithm to deal with arbitrary data rates and discovering multidimensional motifs.
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