A Unified Time Series Event Detection Framework
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Documentation for package ‘harbinger’ version 2.1.707
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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 |