Package: harbinger 2.1.707

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], Anthony Heimlich [aut], Antonio Castro [aut], Antonio Mello [aut], Diego Carvalho [ctb], Eduardo Bezerra [ctb], Ellen Paixão [aut], Fernando Fraga [aut], Gabriel Giuliano [aut], Heraldo Borges [aut], Igor Andrade [aut], Isabele Rocha [aut], Janio Lima [aut], Jessica Souza [aut], Lais Baroni [aut], Lucas Tavares [aut], Michel Reis [aut], Rebecca Salles [aut], CEFET/RJ [cph]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
harbinger/json (API)

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

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

Pkgdown/docs site:https://cefet-rj-dal.github.io

Datasets:

On CRAN:

Conda:

anomaly-detectionchange-point-detectionevent-detectionevent-detection-metricmotif-discovery

7.18 score 33 stars 756 scripts 553 downloads 76 exports 190 dependencies

Last updated from:721845bdf8. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK326
source / vignettesOK286
linux-release-x86_64OK322
macos-release-arm64OK132
macos-oldrel-arm64OK172
windows-develOK230
windows-releaseOK184
windows-oldrelOK186
wasm-releaseOK180

Exports:collect_batch_logcollect_detectioncollect_tracedetecthan_autoencoderhanc_mlhanct_dtwhanct_kmeanshanr_arimahanr_emdhanr_fbiadhanr_ffthanr_fft_amochanr_fft_amoc_cusumhanr_fft_binseghanr_fft_binseg_cusumhanr_fft_smahanr_garchhanr_histogramhanr_mlhanr_remdhanr_rtadhanr_wavelethar_ensemblehar_ensemble_fuzzyhar_ensemble_plothar_ensemble_plot_modelshar_evalhar_eval_softhar_memory_fullhar_memory_last_observationshar_memory_slidinghar_online_detect_onlyhar_online_incrementalhar_online_refit_fullhar_online_sessionhar_plothar_source_callbackhar_source_dataframehar_source_kafkahar_source_simulatedhar_stream_evalhar_stream_experimentharbingerharutilshcp_amochcp_binseghcp_bocpdhcp_cf_arimahcp_cf_etshcp_cf_lrhcp_chowhcp_garchhcp_gfthcp_joinpointhcp_kswinhcp_page_hinkleyhcp_pelthcp_scphcp_waypointhdis_mphdis_saxhmo_mphmo_saxhmo_xsaxhmu_pcaingestis_finishedloadfulldatamasnext_observationrun_onlinesource_infostep_onlinetrans_saxtrans_xsax

Dependencies:arulesarulesSequencesaskpassaudiobackportsbase64encbitbit64bootbslibcachemcaretcellrangerchangepointcheckmatechronclassclicliprclockclueclustercodetoolscolorspacecommonmarkcpp11crayoncurldaltoolboxdata.tabledbscanDescToolsdiagramdigestDistributionUtilsdoSNOWdotCall64dplyrdtwdtwcluste1071elmNNRcppEMDExactexpmfarverfastmapfieldsflexclustFNNfontawesomeforcatsforeachforecastfracdifffsfuturefuture.applyGeneralizedHyperbolicgenericsggplot2ggrepelgldglobalsgluegowergtablehardhathavenhhthmshtmltoolshttpuvhttripredisobanditeratorsjquerylibjsonliteKernelKnnkernlabKernSmoothKFASkslabelinglaterlatticelavalifecyclelistenvlmomlmtestlocfitlubridatemagrittrmapsMASSMatrixmclustmemoisemFiltermgcvmimeModelMetricsmodeltoolsmulticoolmvtnormnlmenloptrnnetnumDerivopensslotelparallellypatchworkpillarpkgconfigplyrpracmaprettyunitspROCprodlimprogressprogressrpromisesproxypurrrR6randomForestrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppHungarianRcppParallelRcppThreadreadrreadxlrecipesrematchreshapereshape2RJSONIOrlangrootSolverpartRsolnpRSpectrarstudioapirugarchS7sandwichsassscalesshapeshinyshinyjsSkewHyperbolicsnowsourcetoolsspamsparsevctrsspdSQUAREMstringistringrstrucchangesurvivalsystibbletidyrtidyselecttimechangetimeDatetreetruncnormtsmptspredittzdburcautf8vctrsviridisLitevroomwaveletswithrxtablextszoo

Readme and manuals

Help Manual

Help pageTopics
Yahoo Webscope S5 – A1 Benchmark (Real)A1Benchmark
Yahoo Webscope S5 – A2 Benchmark (Synthetic)A2Benchmark
Yahoo Webscope S5 – A3 Benchmark (Synthetic with Outliers)A3Benchmark
Yahoo Webscope S5 – A4 Benchmark (Synthetic with Anomalies and CPs)A4Benchmark
Collect batch execution logcollect_batch_log
Collect final detection outputcollect_detection
Collect the online tracecollect_trace
Detect events in time seriesdetect
Detect events using Harbinger Fuzzy Ensembledetect.har_ensemble_fuzzy
Evaluate a streaming traceevaluate.har_stream_eval
Time series for anomaly detectionexamples_anomalies
Time series for change point detectionexamples_changepoints
Time series for event detectionexamples_harbinger
Time series for motif/discord discoveryexamples_motifs
Fit an online sessionfit.har_online_session
GECCO Challenge 2018 – Water Quality Time Seriesgecco
Anomaly detector using autoencodershan_autoencoder
Anomaly detector based on ML classificationhanc_ml
Anomaly detector using DTWhanct_dtw
Anomaly detector using k-meanshanct_kmeans
Anomaly detector using ARIMAhanr_arima
Anomaly detector using EMDhanr_emd
Anomaly detector using FBIADhanr_fbiad
Anomaly detector using FFThanr_fft
Anomaly Detector using FFT with AMOC Cutoffhanr_fft_amoc
Anomaly Detector using FFT with AMOC and CUSUM Cutoffhanr_fft_amoc_cusum
Anomaly Detector using FFT with Binary Segmentation Cutoffhanr_fft_binseg
Anomaly Detector using FFT with BinSeg and CUSUM Cutoffhanr_fft_binseg_cusum
Anomaly Detector using Adaptive FFT and Moving Averagehanr_fft_sma
Anomaly detector using GARCHhanr_garch
Anomaly detector using histogramshanr_histogram
Anomaly detector based on ML regressionhanr_ml
Anomaly detector using REMDhanr_remd
Resilient Transformation Anomaly Detector (RTAD)hanr_rtad
Anomaly detector using Waveletshanr_wavelet
Harbinger Ensemblehar_ensemble
Harbinger Fuzzy Ensemblehar_ensemble_fuzzy
Plot Harbinger Ensemble Outputshar_ensemble_plot
Plot individual model detections in a Harbinger fuzzy ensemblehar_ensemble_plot_models
Evaluation of event detectionhar_eval
Evaluation of event detection (SoftED)har_eval_soft
Full memory policyhar_memory_full
Last-observation memory policyhar_memory_last_observations
Sliding batch memory policyhar_memory_sliding
Detect-only execution strategyhar_online_detect_only
Online execution strategieshar_online_executor
Incremental execution strategyhar_online_incremental
Online memory policieshar_online_memory
Full refit execution strategyhar_online_refit_full
Harbinger online sessionhar_online_session
Streaming data sources for Harbingerhar_online_sources
Plot event detection on a time serieshar_plot
Callback sourcehar_source_callback
Data-frame sourcehar_source_dataframe
Kafka source stubhar_source_kafka
Simulated sourcehar_source_simulated
Streaming evaluation for online detectionhar_stream_eval
Streaming experiment runnerhar_stream_experiment
Harbingerharbinger
Harbinger Utilitiesharutils
At Most One Change (AMOC)hcp_amoc
Binary Segmentation (BinSeg)hcp_binseg
Bayesian Online Change Point Detectionhcp_bocpd
Change Finder using ARIMAhcp_cf_arima
Change Finder using ETShcp_cf_ets
Change Finder using Linear Regressionhcp_cf_lr
Chow Test (structural break)hcp_chow
Change Finder using GARCHhcp_garch
Generalized Fluctuation Test (GFT)hcp_gft
Joinpoint Regression++ change-point detectorhcp_joinpoint
KSWIN change-point detectorhcp_kswin
Page-Hinkley change-point detectorhcp_page_hinkley
Pruned Exact Linear Time (PELT)hcp_pelt
Seminal change pointhcp_scp
Waypoint: adaptive change-point detection with autoencoder and CUSUMhcp_waypoint
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
Add observations to an online sessioningest
Test whether an online session has finishedis_finished
Load full dataset from mini data objectloadfulldata
Moving average smoothingmas
MIT-BIH Arrhythmia Database – MLII Leadmit_bih_MLII
MIT-BIH Arrhythmia Database – V1 Leadmit_bih_V1
MIT-BIH Arrhythmia Database – V2 Leadmit_bih_V2
MIT-BIH Arrhythmia Database – V5 Leadmit_bih_V5
Numenta Anomaly Benchmark (NAB) – artificialWithAnomalynab_artificialWithAnomaly
Numenta Anomaly Benchmark (NAB) – realAdExchangenab_realAdExchange
Numenta Anomaly Benchmark (NAB) realAWSCloudwatchnab_realAWSCloudwatch
Numenta Anomaly Benchmark (NAB) realKnownCausenab_realKnownCause
Numenta Anomaly Benchmark (NAB) realTrafficnab_realTraffic
Numenta Anomaly Benchmark (NAB) realTweetsnab_realTweets
Get the next observation from a sourcenext_observation
Oil Wells Dataset – Type 1oil_3w_Type_1
Oil Wells Dataset – Type 2oil_3w_Type_2
Oil Wells Dataset – Type 4oil_3w_Type_4
Oil Wells Dataset – Type 5oil_3w_Type_5
Oil Wells Dataset – Type 6oil_3w_Type_6
Oil Wells Dataset – Type 7oil_3w_Type_7
Oil Wells Dataset – Type 8oil_3w_Type_8
Run an online sessionrun_online
Retrieve source metadatasource_info
Step an online session oncestep_online
SAX transformationtrans_sax
XSAX transformationtrans_xsax
UCR Anomaly Archive – ECGucr_ecg
UCR Anomaly Archive – Internal Bleedingucr_int_bleeding
UCR Anomaly Archive – NASA Spacecraftucr_nasa
UCR Anomaly Archive – Italian Power Demanducr_power_demand