Package: harbinger 1.0.787

Eduardo Ogasawara

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>.

Authors:Eduardo Ogasawara [aut, ths, cre], 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 [cph]

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harbinger.pdf |harbinger.html
harbinger/json (API)

# Install 'harbinger' in R:
install.packages('harbinger', repos = c('https://cefet-rj-dal.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cefet-rj-dal/harbinger/issues

Datasets:

On CRAN:

6.47 score 14 stars 82 scripts 687 downloads 39 exports 175 dependencies

Last updated 4 months agofrom:b9f0921143. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 11 2024
R-4.5-winOKNov 11 2024
R-4.5-linuxOKNov 11 2024
R-4.4-winOKNov 11 2024
R-4.4-macOKNov 11 2024
R-4.3-winOKNov 11 2024
R-4.3-macOKNov 11 2024

Exports:detecthan_autoencoderhanc_mlhanct_dtwhanct_kmeanshanr_arimahanr_emdhanr_fbiadhanr_ffthanr_garchhanr_histogramhanr_mlhanr_redhanr_remdhanr_wavelethar_evalhar_eval_softhar_plotharbingerhcp_amochcp_binseghcp_cf_arimahcp_cf_etshcp_cf_lrhcp_chowhcp_garchhcp_gfthcp_pelthcp_redhcp_scphdis_mphdis_saxhmo_mphmo_saxhmo_xsaxhmu_pcamastrans_saxtrans_xsax

Dependencies:audiobackportsbase64encbitopsbslibcachemcaretcaToolschangepointcheckmatechronclasscliclockclueclustercodetoolscolorspacecommonmarkcpp11crayoncurldaltoolboxdata.tabledbscandiagramdigestDistributionUtilsdoSNOWdotCall64dplyrdtwdtwcluste1071elmNNRcppEMDfansifarverfastmapfieldsflexclustFNNfontawesomeforeachforecastfracdifffsfuturefuture.applyGeneralizedHyperbolicgenericsggplot2ggrepelglobalsgluegowergplotsgtablegtoolshardhatherehhthmshtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernelKnnkernlabKernSmoothkslabelinglaterlatticelavalifecyclelistenvlmtestlocfitlubridatemagrittrmapsMASSMatrixmclustmemoisemgcvmimeMLmetricsModelMetricsmodeltoolsmulticoolmunsellmvtnormnlmenloptrnnetnumDerivparallellypillarpkgconfigplyrpngpracmaprettyunitspROCprodlimprogressprogressrpromisesproxypurrrquadprogquantmodR6randomForestrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppThreadRcppTOMLrecipesreshapereshape2reticulateRJSONIOrlangROCRrpartrprojrootRsolnpRSpectrarugarchsandwichsassscalesshapeshinyshinyjsSkewHyperbolicsnowsourcetoolsspamspdSQUAREMstringistringrstrucchangesurvivaltibbletidyrtidyselecttimechangetimeDatetreetruncnormtseriestsmpTTRtzdburcautf8vctrsviridisLitewaveletswithrxtablextszoo

Readme and manuals

Help Manual

Help pageTopics
Detect events in time seriesdetect
Time series for anomaly detectionexamples_anomalies
Time series for change point detectionexamples_changepoints
Time series for event detectionexamples_harbinger
Time series for change point detectionexamples_motifs
Anomaly detector using autoencoderhan_autoencoder
Anomaly detector based on machine learning classificationhanc_ml
Anomaly detector using DTWhanct_dtw
Anomaly detector using kmeanshanct_kmeans
Anomaly detector using ARIMA.hanr_arima
Anomaly detector using EMDhanr_emd
Anomaly detector using FBIADhanr_fbiad
Anomaly detector using FFThanr_fft
Anomaly detector using GARCHhanr_garch
Anomaly detector using histogramhanr_histogram
Anomaly detector based on machine learning regression.hanr_ml
Anomaly and change point detector using REDhanr_red
Anomaly detector using REMDhanr_remd
Anomaly detector using Wavelethanr_wavelet
Evaluation of event detectionhar_eval
Evaluation of event detectionhar_eval_soft
Plot event detection on a time serieshar_plot
Harbingerharbinger
At most one change (AMOC) methodhcp_amoc
Binary segmentation (BinSeg) methodhcp_binseg
Change Finder using ARIMAhcp_cf_arima
Change Finder using ETShcp_cf_ets
Change Finder using LRhcp_cf_lr
Chow test methodhcp_chow
Change Finder using GARCHhcp_garch
Generalized Fluctuation Test (GFT)hcp_gft
Pruned exact linear time (PELT) methodhcp_pelt
Anomaly and change point detector using REDhcp_red
Seminal change pointhcp_scp
Discord discovery using Matrix Profilehdis_mp
Discord discovery using SAXhdis_sax
Motif discovery using Matrix Profilehmo_mp
Motif discovery using SAXhmo_sax
Motif discovery using xsaxhmo_xsax
Multivariate anomaly detector using PCAhmu_pca
Moving average smoothingmas
SAXtrans_sax
XSAXtrans_xsax