examples_motifs

A list of time series for change point<ul>
<li>simple: a simple synthetic time series</li>
<li>mitdb100: sample of mitdb 100 time series</li>
<li>mitdb102: sample of mitdb 102 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_motifs function

A list of time series for motif discovery.

Format

Time series for change point detection — examples_motifs

Time series for change point detection

examples_motifs: Time series for change point detection

Description

Usage

Arguments

Format

References

Examples