A Unified Time Series Event Detection Framework


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Documentation for package ‘harbinger’ version 1.2.717

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detect Detect events in time series
examples_anomalies Time series for anomaly detection
examples_changepoints Time series for change point detection
examples_harbinger Time series for event detection
examples_motifs Time series for change point detection
hanc_ml Anomaly detector based on machine learning classification
hanr_arima Anomaly detector using ARIMA.
hanr_emd Anomaly detector using EMD
hanr_fft Anomaly detector using FFT
hanr_fft_amoc Anomaly Detector using FFT with AMOC Cutoff
hanr_fft_amoc_cusum Anomaly Detector using FFT with AMOC and CUSUM Cutoff
hanr_fft_binseg Anomaly Detector using FFT with Binary Segmentation Cutoff
hanr_fft_binseg_cusum Anomaly Detector using FFT with BinSeg and CUSUM Cutoff
hanr_garch Anomaly detector using GARCH
hanr_histogram Anomaly detector using histogram
hanr_red Anomaly and change point detector using RED
hanr_wavelet Anomaly detector using Wavelet
harbinger Harbinger
harutils Harbinger Utils
har_ensemble Harbinger Ensemble
har_eval Evaluation of event detection
har_eval_soft Evaluation of event detection
har_plot Plot event detection on a time series
hcp_amoc At most one change (AMOC) method
hcp_binseg Binary segmentation (BinSeg) method
hcp_cf_arima Change Finder using ARIMA
hcp_cf_ets Change Finder using ETS
hcp_cf_lr Change Finder using LR
hcp_chow Chow test method
hcp_garch Change Finder using GARCH
hcp_gft Generalized Fluctuation Test (GFT)
hcp_pelt Pruned exact linear time (PELT) method
hdis_mp Discord discovery using Matrix Profile
hmo_mp Motif discovery using Matrix Profile
hmu_pca Multivariate anomaly detector using PCA
mas Moving average smoothing
trans_sax SAX
trans_xsax XSAX