Time series / Dataset
Numenta Anomaly Benchmark (NAB)
58 labelled real-world and artificial time-series data files (1,000-22,000 points each, 365,551 points in total) from sources such as AWS server metrics, Twitter volume, advertisement clicks and traffic data, with hand-labelled anomaly windows and a scoring scheme for streaming detection. No train/test split: detectors run online over each file and the first 15% of each file is an unscored probationary period.
- Also known as
- NAB, Numenta Anomaly Benchmark, Numenta, NAB v1.0, NAB v1.1
- Domain
- mixed (server metrics, web traffic, social media, sensors, artificial)
- License
- MIT (repository licence)
- Test items from
- 2015-10-09
Evidence
“October 9, 2015”
arxiv.org · paper · title page · retrieved 2026-10-15 “The repository contains all the code, data, and detailed results reported in this paper.”
arxiv.org · paper · title page · retrieved 2026-10-15 - Homepage
- github.com
Exposure by model
| Benchmark | Model | Level | Reason |
|---|---|---|---|
| Time-series anomaly detection | Chronos-2-Synth | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Chronos-2 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Chronos-Bolt | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Chronos (T5) | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | DADA (General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders) | a contamination analysis found test examples of the related dataset TSB-AD-U (TSB-AD univariate) (Third-party (TimeRCD authors): DADA's pre-training corpus (IOPS, MGAB, YAHOO) heavily overlaps their zero-shot test benchmarks drawn from TSB-AD.) [third-party study] | |
| Time-series anomaly detection | Granite-TimeSeries-TTM-R1 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Granite-TimeSeries-TTM-R2.1 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Granite-TimeSeries-TTM-R2 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Granite-TTM-R3 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Lag-Llama | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Mantis (Mantis-8M) | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Mantis+ (MantisPlus) | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | MantisV2 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Moirai-1.0-R | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Moirai-1.1-R | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Moirai-2.0-R | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Moirai-MoE-1.0-R | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | MOMENT-1 | Training data seen | only the training split was used, not the benchmark's test data (via Time Series Pile) |
| Time-series anomaly detection | NuTime | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Sundial (Timer 3.0) | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Time-MoE | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Time-RCD (TimeRCD) | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Timer-S1 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Timer-XL (timer-base-84m) | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Timer | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | TimeRadar | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TimesFM 1.0 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TimesFM 2.0 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TimesFM 2.5 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TimesFM 3.0 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TiRex 1.1 (gifteval) | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TiRex-2-fevbench (TiRex-2-f) | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | TiRex-2-gifteval-pretrain (TiRex-2-gp) | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TiRex-2-gifteval-zs (TiRex-2-g) | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | TiRex-2 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TiRex | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | Toto-Open-Base-1.0 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Toto-2.0-2.5B-FT | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Toto 2.0 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Toto (2024 technical report) | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | TSPulse r1 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | TTM-R3 | Unknown | training data not disclosed in enough detail |
| Time-series anomaly detection | Tiny Time Mixer (TTM) Research-Use R2 | Not seen | absent from the complete list of training data |
| Time-series anomaly detection | UniTS few-shot anomaly detection checkpoints | Not seen | absent from the complete list of training data and from its base models' data |
| Time-series anomaly detection | UniTS | Not seen | absent from the complete list of training data |
Notes
published_from = date of the NAB v1.0 paper, which states the repository contains the data. The numenta/NAB repository was created 2014-05-06, so parts of the corpus may have been public earlier. Current repository version is NAB v1.1.
Sources
- Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark (Lavin & Ahmad, arXiv v4) · paper · retrieved 2026-10-15
- NAB README · readme · retrieved 2026-10-15
- GitHub API metadata of numenta/NAB · code · retrieved 2026-10-15
- Wu & Keogh, Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (arXiv v4; IEEE TKDE) · paper · retrieved 2026-10-15