Time-Series Anomaly Detection Comprehensive Benchmark
This repository updates the comprehensive list of classic and state-of-the-art methods and datasets for Anomaly Decetion in Time-Series. This is part of an onging research at Time Series Analytics Lab, Monash University.
If you use this repository in your works, please cite the main article:
[-] Zamanzadeh Darban, Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2022). Deep Learning for Time Series Anomaly Detection: A Survey. arXiv e-prints. doi:10.48550/ARXIV.2211.05244 [arXiv]
@ARTICLE{2022arXiv221105244Z,
author = {Zamanzadeh Darban, Zahra and Webb, Geoffrey I. and Pan, Shirui and Aggarwal, Charu C. and Salehi, Mahsa},
title = {Deep Learning for Time Series Anomaly Detection: A Survey},
journal = {arXiv e-prints},
year = 2022,
month = Nov,
eid = {arXiv:2211.05244},
eprint = {2211.05244},
url = {https://arxiv.org/abs/2211.05244},
doi = {10.48550/ARXIV.2211.05244},
}
- Revisiting Time Series Outlier Detection: Definitions and Benchmarks, NeurIPS 2021.
- Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress, TKDE, 2021.
- Towards a Rigorous Evaluation of Time-Series Anomaly Detection, AAAI 2022.
- Anomaly detection in time series: a comprehensive evaluation, VLDB 2022.
| Dataset/Benchmark | Real/Synth | MTS/UTS | # Samples | # Entities | # Dim | Domain |
|---|---|---|---|---|---|---|
| CalIt2 | Real | MTS | 10,080 | 2 | 2 | Urban events management |
| CAP | Real | MTS | 921,700,000 | 108 | 21 | Medical and health |
| CICIDS2017 | Real | MTS | 2,830,540 | 15 | 83 | Server machines monitoring |
| Credit Card fraud detection | Real | MTS | 284,807 | 1 | 31 | Fraud detectcion |
| DMDS | Real | MTS | 725,402 | 1 | 32 | Industrial Control Systems |
| Engine Dataset | Real | MTS | NA | NA | 12 | Industrial control systems |
| Exathlon | Real | MTS | 47,530 | 39 | 45 | Server machines monitoring |
| GECCO IoT | Real | MTS | 139,566 | 1 | 9 | Internet of things (IoT) |
| Genesis | Real | MTS | 16,220 | 1 | 18 | Industrial control systems |
| GHL | Synth | MTS | 200,001 | 48 | 22 | Industrial control systems |
| IOnsphere | Real | MTS | 351 | 32 | Astronomical studies | |
| KDDCUP99 | Real | MTS | 4,898,427 | 5 | 41 | Computer networks |
| Kitsune | Real | MTS | 3,018,973 | 9 | 115 | Computer networks |
| MBD | Real | MTS | 8,640 | 5 | 26 | Server machines monitoring |
| Metro | Real | MTS | 48,204 | 1 | 5 | Urban events management |
| MIT-BIH Arrhythmia (ECG) | Real | MTS | 28,600,000 | 48 | 2 | Medical and health |
| MIT-BIH-SVDB | Real | MTS | 17,971,200 | 78 | 2 | Medical and health |
| MMS | Real | MTS | 4,370 | 50 | 7 | Server machines monitoring |
| MSL | Real | MTS | 132,046 | 27 | 55 | Aerospace |
| NAB-realAdExchange | Real | MTS | 9,616 | 3 | 2 | Business |
| NAB-realAWSCloudwatch | Real | MTS | 67,644 | 1 | 17 | Server machines monitoring |
| NASA Shuttle Valve Data | Real | MTS | 49,097 | 1 | 9 | Aerospace |
| OPPORTUNITY | Real | MTS | 869,376 | 24 | 133 | Computer networks |
| Pooled Server Metrics (PSM) | Real | MTS | 132,480 | 1 | 24 | Server machines monitoring |
| PUMP | Real | MTS | 220,302 | 1 | 44 | Industrial control systems |
| SMAP | Real | MTS | 562,800 | 55 | 25 | Environmental management |
| SMD | Real | MTS | 1,416,825 | 28 | 38 | Server machines monitoring |
| SWAN-SF | Real | MTS | 355,330 | 5 | 51 | Astronomical studies |
| SWaT | Real | MTS | 946,719 | 1 | 51 | Industrial control systems |
| WADI | Real | MTS | 957,372 | 1 | 127 | Industrial control systems |
| NYC Bike | Real | MTS/UTS | +25M | NA | NA | Urban events management |
| NYC Taxi | Real | MTS/UTS | +200M | NA | NA | Urban events management |
| UCR | Real/Synth | MTS/UTS | NA | NA | NA | Multiple domains |
| Dodgers Loop Sensor Dataset | Real | UTS | 50,400 | 1 | 1 | Urban events management |
| IOPS | Real | UTS | 2,918,821 | 29 | 1 | Business |
| KPI AIOPS | Real | UTS | 5,922,913 | 58 | 1 | Business |
| MGAB | Synth | UTS | 100,000 | 10 | 1 | Medical and health |
| MIT-BIH-LTDB | Real | UTS | 67,944,954 | 7 | 1 | Medical and health |
| NAB-artificialNoAnomaly | Synth | UTS | 20,165 | 5 | 1 | - |
| NAB-artificialWithAnomaly | Synth | UTS | 24,192 | 6 | 1 | - |
| NAB-realKnownCause | Real | UTS | 69,568 | 7 | 1 | Multiple domains |
| NAB-realTraffic | Real | UTS | 15,662 | 7 | 1 | Urban events management |
| NAB-realTweets | Real | UTS | 158,511 | 10 | 1 | Business |
| NeurIPS-TS | Synth | UTS | NA | 1 | 1 | - |
| NormA | Real/Synth | UTS | 1,756,524 | 21 | 1 | Multiple domains |
| Power Demand Dataset | Real | UTS | 35,040 | 1 | 1 | Industrial control systems |
| SensoreScope | Real | UTS | 621,874 | 23 | 1 | Internet of things (IoT) |
| Space Shuttle Dataset | Real | UTS | 15,000 | 15 | 1 | Aerospace |
| Yahoo | Real/Synth | UTS | 572,966 | 367 | 1 | Multiple domains |
| A1 | MA2 | Model | Su/Un3 | Input | P/S4 | Code |
|---|---|---|---|---|---|---|
| Forecasting | RNN | LSTM-AD [1] | Un | P | Point | Github |
| Forecasting | RNN | LSTM RNN [2] | Semi | P | Subseq | |
| Forecasting | RNN | LSTM-based [3] | Un | W | - | |
| Forecasting | RNN | TCQSA [4] | Su | P | - | |
| Forecasting | HTM | Numenta HTM [5] | Un | - | - | |
| Forecasting | HTM | Multi HTM [6] | Un | - | - | Github |
| Forecasting | CNN | SR-CNN [7] | Un | W | Point + Subseq | Github |
| Reconstruction | VAE | Donut [8] | Un | W | Subseq | Github |
| Reconstruction | VAE | Buzz [9] | Un | W | Subseq | |
| Reconstruction | VAE | Bagel [10] | Un | W | Subseq | Github |
| Reconstruction | AE | EncDec-AD [11] | Semi | W | Point | Github |
| A1 | MA2 | Model | T/S3 | Su/Un4 | Input | Int5 | P/S6 | Code |
|---|---|---|---|---|---|---|---|---|
| Forecasting | RNN | LSTM-NDT [12] | T | Un | W | ✓ | Subseq | |
| Forecasting | RNN | DeepLSTM [13] | T | Semi | P | Point | ||
| Forecasting | RNN | LSTM-PRED [14] | T | Un | W | ✓ | - | |
| Forecasting | RNN | LGMAD [15] | T | Semi | P | Point | ||
| Forecasting | RNN | THOC [16] | T | Self | W | Subseq | ||
| Forecasting | RNN | AD-LTI [17] | T | Un | P | Point (frame) | ||
| Forecasting | CNN | DeepAnt [18] | T | Un | W | Point + Subseq | Github | |
| Forecasting | CNN | TCN-ms [19] | T | Semi | W | Subseq | ||
| Forecasting | GNN | GDN [20] | S | Un | W | ✓ | - | |
| Forecasting | GNN | GTA* [21] | ST | Semi | - | - | ||
| Forecasting | GNN | GANF [22] | ST | Un | W | |||
| Forecasting | HTM | RADM [23] | T | Un | W | - | ||
| Forecasting | Transformer | SAND [24] | T | Semi | W | - | Github | |
| Forecasting | Transformer | GTA* [21] | ST | Semi | - | - | ||
| Reconstruction | AE | AE/DAE [25] | T | Semi | P | Point | Github | |
| Reconstruction | AE | DAGMM [26] | S | Un | P | Point | ||
| Reconstruction | AE | MSCRED [27] | ST | Un | W | ✓ | Subseq | Github |
| Reconstruction | AE | USAD [28] | T | Un | W | Point | ||
| Reconstruction | AE | APAE [29] | T | Un | W | - | ||
| Reconstruction | AE | RANSynCoders [30] | ST | Un | P | ✓ | Point | |
| Reconstruction | AE | CAE-Ensemble [31] | T | Un | W | Subseq | ||
| Reconstruction | AE | AMSL [32] | T | Self | W | - | ||
| Reconstruction | VAE | LSTM-VAE [33] | T | Semi | P | - | Github | |
| Reconstruction | VAE | OmniAnomaly [34] | T | Un | W | ✓ | Point + Subseq | Github |
| Reconstruction | VAE | STORN [35] | ST | Un | P | Point | ||
| Reconstruction | VAE | GGM-VAE [36] | T | Un | W | Subseq | ||
| Reconstruction | VAE | SISVAE [37] | T | Un | W | Point | ||
| Reconstruction | VAE | VAE-GAN [38] | T | Semi | W | Point | ||
| Reconstruction | VAE | VELC [39] | T | Un | - | - | ||
| Reconstruction | VAE | TopoMAD [40] | ST | Un | W | Subseq | ||
| Reconstruction | VAE | PAD [41] | T | Un | W | Subseq | ||
| Reconstruction | VAE | InterFusion [42] | ST | Un | W | ✓ | Subseq | |
| Reconstruction | VAE | MT-RVAE* [43] | ST | Un | W | - | ||
| Reconstruction | VAE | RDSMM [44] | T | Un | W | Point + Subseq | ||
| Reconstruction | GAN | MAD-GAN [45] | ST | Un | W | Subseq | ||
| Reconstruction | GAN | BeatGAN [46] | T | Un | W | Subseq | ||
| Reconstruction | GAN | DAEMON [47] | T | Un | W | ✓ | Subseq | |
| Reconstruction | GAN | FGANomaly [48] | T | Un | W | Point + Subseq | ||
| Reconstruction | GAN | DCT-GAN* [49] | T | Un | W | - | ||
| Reconstruction | Transformer | Anomaly Transformer [50] | T | Un | W | Subseq | ||
| Reconstruction | Transformer | TranAD [51] | T | Un | W | ✓ | Subseq | |
| Reconstruction | Transformer | DCT-GAN* [49] | T | Un | W | - | ||
| Reconstruction | Transformer | MT-RVAE* [43] | ST | Un | W | - | ||
| Hybrid | AE | CAE-M [52] | ST | Un | W | Subseq | ||
| Hybrid | AE | NSIBF* [53] | T | Un | W | Subseq | ||
| Hybrid | RNN | NSIBF* [53] | T | Un | W | Subseq | ||
| Hybrid | RNN | TAnoGAN [54] | T | Un | W | Subseq | Github | |
| Hybrid | GNN | MTAD-GAT [55] | ST | Self | W | ✓ | Subseq | Github |
| Hybrid | GNN | FuSAGNet [56] | ST | Semi | W | Subseq |
1: Approach.
2: Main Approach.
3: Temporal/Spatial
4: Supervised/Unsupervised | Values: [Su: Supervised, Un: Unsupervised, Semi: Semi-supervised, Self: Self-supervised].
5: Interpretability
6: Point/Sub-sequence
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