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>.

Version:	1.2.767
Depends:	R (≥ 4.1.0)
Imports:	tspredit, changepoint, daltoolbox, forecast, ggplot2, hht, RcppHungarian, dplyr, dtwclust, rugarch, stats, stringr, strucchange, tsmp, wavelets, zoo
Published:	2026-02-11
DOI:	10.32614/CRAN.package.harbinger
Author:	Eduardo Ogasawara [aut, ths, cre], Antonio Castro [aut], Antonio Mello [aut], Diego Carvalho [ctb], Eduardo Bezerra [ctb], Ellen Paixão [aut], Fernando Fraga [aut], Heraldo Borges [aut], Janio Lima [aut], Jessica Souza [aut], Lais Baroni [aut], Lucas Tavares [aut], Michel Reis [aut], Rebecca Salles [aut], CEFET/RJ [cph]
Maintainer:	Eduardo Ogasawara <eogasawara at ieee.org>
BugReports:	https://github.com/cefet-rj-dal/harbinger/issues
License:	MIT + file LICENSE
URL:	https://cefet-rj-dal.github.io/harbinger/, https://github.com/cefet-rj-dal/harbinger
NeedsCompilation:	no
Materials:	README
CRAN checks:	harbinger results

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