examples_anomalies

A list of time series for anomaly detection<ul>
<li>simple: a simple synthetic time series</li>
<li>contextual: a contextual synthetic time series</li>
<li>simple: a trend synthetic time series</li>
<li>trend: a simple synthetic time series</li>
<li>multiple: a multiple anomalies synthetic time series</li>
<li>sequence: a sequence synthetic time series</li>
<li>tt: a train-test synthetic time series</li>
<li>tt_warped: a warped train-test synthetic time series</li>
</ul>#'

datasets

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>.

Eduardo Ogasawara

harbinger

A Unified Time Series Event Detection Framework

Antonio Castro

Antonio Mello

Ellen Paixão

Fernando Fraga

Heraldo Borges

Janio Lima

Jessica Souza

Lais Baroni

Lucas Tavares

Michel Reis

Rebecca Salles

Diego Carvalho

Eduardo Bezerra

Rafaelli Coutinho

Esther Pacitti

Fabio Porto

CEFET/RJ 

examples_anomalies function

A list of time series for anomaly detection.

Format

Time series for anomaly detection — examples_anomalies

Time series for anomaly detection

examples_anomalies: Time series for anomaly detection

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References

Examples