harbinger1.2.747 package

A Unified Time Series Event Detection Framework

detect

Detect events in time series

han_autoencoder

Anomaly detector using autoencoders

hanc_ml

Anomaly detector based on ML classification

hanct_dtw

Anomaly detector using DTW

hanct_kmeans

Anomaly detector using kmeans

hanr_arima

Anomaly detector using ARIMA

hanr_emd

Anomaly detector using EMD

hanr_fbiad

Anomaly detector using FBIAD

hanr_fft_amoc_cusum

Anomaly Detector using FFT with AMOC and CUSUM Cutoff

hanr_fft_amoc

Anomaly Detector using FFT with AMOC Cutoff

hanr_fft_binseg_cusum

Anomaly Detector using FFT with BinSeg and CUSUM Cutoff

hanr_fft_binseg

Anomaly Detector using FFT with Binary Segmentation Cutoff

hanr_fft_sma

Anomaly Detector using Adaptive FFT and Moving Average

hanr_fft

Anomaly detector using FFT

hanr_garch

Anomaly detector using GARCH

hanr_histogram

Anomaly detector using histograms

hanr_ml

Anomaly detector based on ML regression

hanr_remd

Anomaly detector using REMD

hanr_rtad

Anomaly and change point detector using RTAD

hanr_wavelet

Anomaly detector using Wavelets

har_ensemble

Harbinger Ensemble

har_eval_soft

Evaluation of event detection (SoftED)

har_eval

Evaluation of event detection

har_plot

Plot event detection on a time series

harbinger

Harbinger

harutils

Harbinger Utilities

hcp_amoc

At Most One Change (AMOC)

hcp_binseg

Binary Segmentation (BinSeg)

hcp_cf_arima

Change Finder using ARIMA

hcp_cf_ets

Change Finder using ETS

hcp_cf_lr

Change Finder using Linear Regression

hcp_chow

Chow Test (structural break)

hcp_garch

Change Finder using GARCH

hcp_gft

Generalized Fluctuation Test (GFT)

hcp_pelt

Pruned Exact Linear Time (PELT)

hcp_scp

Seminal change point

hdis_mp

Discord discovery using Matrix Profile

hdis_sax

Discord discovery using SAX

hmo_mp

Motif discovery using Matrix Profile

hmo_sax

Motif discovery using SAX

hmo_xsax

Motif discovery using XSAX

hmu_pca

Multivariate anomaly detector using PCA

mas

Moving average smoothing

trans_sax

SAX transformation

trans_xsax

XSAX transformation

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>.

  • Maintainer: Eduardo Ogasawara
  • License: MIT + file LICENSE
  • Last published: 2025-10-27