R: A Unified Time Series Event Detection Framework

# A Unified Time Series Event Detection Framework

## Documentation for package ‘harbinger’ version 2.1.707

- DESCRIPTION file
- Package README

## Help Pages

A C D E F G H I L M N O R S T U

## -- A --

A1Benchmark | Yahoo Webscope S5 – A1 Benchmark (Real)
A2Benchmark | Yahoo Webscope S5 – A2 Benchmark (Synthetic)
A3Benchmark | Yahoo Webscope S5 – A3 Benchmark (Synthetic with Outliers)
A4Benchmark | Yahoo Webscope S5 – A4 Benchmark (Synthetic with Anomalies and CPs)

## -- C --

collect_batch_log | Collect batch execution log
collect_detection | Collect final detection output
collect_trace | Collect the online trace

## -- D --

detect | Detect events in time series
detect.har_ensemble_fuzzy | Detect events using Harbinger Fuzzy Ensemble

## -- E --

evaluate.har_stream_eval | Evaluate a streaming trace
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 motif/discord discovery

## -- F --

fit.har_online_session | Fit an online session

## -- G --

gecco | GECCO Challenge 2018 – Water Quality Time Series

## -- H --

hanct_dtw | Anomaly detector using DTW
hanct_kmeans | Anomaly detector using k-means
hanc_ml | Anomaly detector based on ML classification
hanr_arima | Anomaly detector using ARIMA
hanr_emd | Anomaly detector using EMD
hanr_fbiad | Anomaly detector using FBIAD
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_fft_sma | Anomaly Detector using Adaptive FFT and Moving Average
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 | Resilient Transformation Anomaly Detector (RTAD)
hanr_wavelet | Anomaly detector using Wavelets
han_autoencoder | Anomaly detector using autoencoders
harbinger | Harbinger
harutils | Harbinger Utilities
har_ensemble | Harbinger Ensemble
har_ensemble_fuzzy | Harbinger Fuzzy Ensemble
har_ensemble_plot | Plot Harbinger Ensemble Outputs
har_ensemble_plot_models | Plot individual model detections in a Harbinger fuzzy ensemble
har_eval | Evaluation of event detection
har_eval_soft | Evaluation of event detection (SoftED)
har_memory_full | Full memory policy
har_memory_last_observations | Last-observation memory policy
har_memory_sliding | Sliding batch memory policy
har_online_detect_only | Detect-only execution strategy
har_online_executor | Online execution strategies
har_online_incremental | Incremental execution strategy
har_online_memory | Online memory policies
har_online_refit_full | Full refit execution strategy
har_online_session | Harbinger online session
har_online_sources | Streaming data sources for Harbinger
har_plot | Plot event detection on a time series
har_source_callback | Callback source
har_source_dataframe | Data-frame source
har_source_kafka | Kafka source stub
har_source_simulated | Simulated source
har_stream_eval | Streaming evaluation for online detection
har_stream_experiment | Streaming experiment runner
hcp_amoc | At Most One Change (AMOC)
hcp_binseg | Binary Segmentation (BinSeg)
hcp_bocpd | Bayesian Online Change Point Detection
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_joinpoint | Joinpoint Regression++ change-point detector
hcp_kswin | KSWIN change-point detector
hcp_page_hinkley | Page-Hinkley change-point detector
hcp_pelt | Pruned Exact Linear Time (PELT)
hcp_scp | Seminal change point
hcp_waypoint | Waypoint: adaptive change-point detection with autoencoder and CUSUM
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

## -- I --

ingest | Add observations to an online session
is_finished | Test whether an online session has finished

## -- L --

loadfulldata | Load full dataset from mini data object

## -- M --

mas | Moving average smoothing
mit_bih_MLII | MIT-BIH Arrhythmia Database – MLII Lead
mit_bih_V1 | MIT-BIH Arrhythmia Database – V1 Lead
mit_bih_V2 | MIT-BIH Arrhythmia Database – V2 Lead
mit_bih_V5 | MIT-BIH Arrhythmia Database – V5 Lead

## -- N --

nab_artificialWithAnomaly | Numenta Anomaly Benchmark (NAB) – artificialWithAnomaly
nab_realAdExchange | Numenta Anomaly Benchmark (NAB) – realAdExchange
nab_realAWSCloudwatch | Numenta Anomaly Benchmark (NAB) realAWSCloudwatch
nab_realKnownCause | Numenta Anomaly Benchmark (NAB) realKnownCause
nab_realTraffic | Numenta Anomaly Benchmark (NAB) realTraffic
nab_realTweets | Numenta Anomaly Benchmark (NAB) realTweets
next_observation | Get the next observation from a source

## -- O --

oil_3w_Type_1 | Oil Wells Dataset – Type 1
oil_3w_Type_2 | Oil Wells Dataset – Type 2
oil_3w_Type_4 | Oil Wells Dataset – Type 4
oil_3w_Type_5 | Oil Wells Dataset – Type 5
oil_3w_Type_6 | Oil Wells Dataset – Type 6
oil_3w_Type_7 | Oil Wells Dataset – Type 7
oil_3w_Type_8 | Oil Wells Dataset – Type 8

## -- R --

run_online | Run an online session

## -- S --

source_info | Retrieve source metadata
step_online | Step an online session once

## -- T --

trans_sax | SAX transformation
trans_xsax | XSAX transformation

## -- U --

ucr_ecg | UCR Anomaly Archive – ECG
ucr_int_bleeding | UCR Anomaly Archive – Internal Bleeding
ucr_nasa | UCR Anomaly Archive – NASA Spacecraft
ucr_power_demand | UCR Anomaly Archive – Italian Power Demand