A Unified Time Series Event Detection Framework
Detect events in time series
Anomaly detector using autoencoders
Anomaly detector based on ML classification
Anomaly detector using DTW
Anomaly detector using kmeans
Anomaly detector using ARIMA
Anomaly detector using EMD
Anomaly detector using FBIAD
Anomaly Detector using FFT with AMOC and CUSUM Cutoff
Anomaly Detector using FFT with AMOC Cutoff
Anomaly Detector using FFT with BinSeg and CUSUM Cutoff
Anomaly Detector using FFT with Binary Segmentation Cutoff
Anomaly Detector using Adaptive FFT and Moving Average
Anomaly detector using FFT
Anomaly detector using GARCH
Anomaly detector using histograms
Anomaly detector based on ML regression
Anomaly detector using REMD
Anomaly and change point detector using RTAD
Anomaly detector using Wavelets
Harbinger Ensemble
Evaluation of event detection (SoftED)
Evaluation of event detection
Plot event detection on a time series
Harbinger
Harbinger Utilities
At Most One Change (AMOC)
Binary Segmentation (BinSeg)
Change Finder using ARIMA
Change Finder using ETS
Change Finder using Linear Regression
Chow Test (structural break)
Change Finder using GARCH
Generalized Fluctuation Test (GFT)
Pruned Exact Linear Time (PELT)
Seminal change point
Discord discovery using Matrix Profile
Discord discovery using SAX
Motif discovery using Matrix Profile
Motif discovery using SAX
Motif discovery using XSAX
Multivariate anomaly detector using PCA
Moving average smoothing
SAX transformation
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>.
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