GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series

@inproceedings{Senin2014GrammarViz2A,
  title={GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series},
  author={Pavel Senin and Jessica Lin and Xing Wang and Tim Oates and Sunil Gandhi and Arnold P. Boedihardjo and Crystal Chen and Susan Frankenstein and Manfred Lerner},
  booktitle={ECML/PKDD},
  year={2014}
}
  • Pavel Senin, Jessica Lin, +6 authors Manfred Lerner
  • Published in ECML/PKDD 2014
  • Computer Science
  • The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 10 REFERENCES

    Mining motifs in massive time series databases

    VIEW 9 EXCERPTS

    HOT SAX: efficiently finding the most unusual time series subsequence

    VIEW 4 EXCERPTS

    Grammar-driven anomaly discovery in time series

    • P. Senin, J. Lin, +5 authors S. Gandhi
    • CSDL Techreport 14-05
    • 2014
    VIEW 3 EXCERPTS

    Visual exploration of frequent patterns in multivariate time series

    VIEW 1 EXCERPT

    Finding Motifs in Time Series

    VIEW 2 EXCERPTS

    Identifying Hierarchical Structure in Sequences: A linear-time algorithm

    VIEW 2 EXCERPTS