GrammarViz 3.0

Autor: Tim Oates, Crystal Chen, Pavel Senin, Sunil Gandhi, Arnold P. Boedihardjo, Jessica Lin, Xing Wang, Susan Frankenstein
Rok vydání: 2018
Předmět:
Zdroj: ACM Transactions on Knowledge Discovery from Data. 12:1-28
ISSN: 1556-472X
1556-4681
DOI: 10.1145/3051126
Popis: The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. Addressing these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.
Databáze: OpenAIRE
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