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harbinger: A Unified Time Series Event Detection Framework
By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.
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| Author | Eduardo Ogasawara [aut, ths, cre] (<https://orcid.org/0000-0002-0466-0626>), Antonio Castro [aut], Antonio Mello [aut], Ellen Paixão [aut], Fernando Fraga [aut], Heraldo Borges [aut], Janio Lima [aut], Jessica Souza [aut], Lais Baroni [aut], Lucas Tavares [aut], Rebecca Salles [aut], Diego Carvalho [aut], Eduardo Bezerra [aut], Rafaelli Coutinho [aut], Esther Pacitti [aut], Fabio Porto [aut], Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) [cph] |
| Maintainer | Eduardo Ogasawara <eogasawara@ieee.org> |
| License | MIT + file LICENSE |
| Version | 1.0.787 |
| URL | https://github.com/cefet-rj-dal/harbinger https://cefet-rj-dal.github.io/harbinger/ |
| Package repository | View on CRAN |
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