An Unified Time Series Event Detection Framework

Documentation for package ‘harbinger’ version 1.0.737

detect	Detect events in time series
hanct_dtw	Anomaly detector using DTW
hanct_kmeans	Anomaly detector using kmeans
hanc_ml	Anomaly detector based on machine learning classification
hanr_arima	Anomaly detector using ARIMA.
hanr_fbiad	Anomaly detector using FBIAD
hanr_garch	Anomaly detector using GARCH
hanr_histogram	Anomaly detector using histogram
hanr_ml	Anomaly detector based on machine learning regression.
han_autoencoder	Anomaly detector using autoencoder
harbinger	Harbinger
har_conv1d	Conv1D
har_eval	Evaluation of event detection
har_eval_soft	Evaluation of event detection
har_examples	Synthetic time series for event detection
har_examples_multi	Synthetic time series for event detection
har_lstm	LSTM
har_plot	Plot event detection on a time series
hcd_page_hinkley	Adapted Page Hinkley method
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
hcp_scp	Seminal change point
hmo_base36	Motif discovery using base36
hmo_mp	Motif discovery using Matrix Profile
hmo_sax	Motif discovery using SAX
hmu_pca	Multivariate anomaly detector using PCA