GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series
Abstract
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 visualization of variable length time series patterns^1.
Cite
Text
Senin et al. "GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44845-8_37Markdown
[Senin et al. "GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/senin2014ecmlpkdd-grammarviz/) doi:10.1007/978-3-662-44845-8_37BibTeX
@inproceedings{senin2014ecmlpkdd-grammarviz,
title = {{GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series}},
author = {Senin, Pavel and Lin, Jessica and Wang, Xing and Oates, Tim and Gandhi, Sunil and Boedihardjo, Arnold P. and Chen, Crystal and Frankenstein, Susan and Lerner, Manfred},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2014},
pages = {468-472},
doi = {10.1007/978-3-662-44845-8_37},
url = {https://mlanthology.org/ecmlpkdd/2014/senin2014ecmlpkdd-grammarviz/}
}