Time series / Dataset
TSB-AD-U (TSB-AD univariate)
Univariate part of the TSB-AD benchmark (Liu & Paparrizos, NeurIPS 2024 D&B): 870 curated, labelled univariate time series from 23 datasets, 12 of which are univariate channels extracted from multivariate datasets (e.g. SMD (U), MSL (U), SMAP (U), SWaT (U)); the others include UCR (anomaly archive), NAB, YAHOO, IOPS, MGAB, WSD, SED, TODS, NEK, Stock and Power. Series were filtered and labels checked or corrected (extended/segmented) by human annotators assisted by detectors. Series-level split: Eval set (350 series, with sampling to balance UCR, YAHOO and WSD) and Tuning set (48 series) for hyperparameter tuning. Within each series, the file name gives a training prefix ('tr_N') that semi-supervised detectors may train on; labels cover the whole series.
- Also known as
- TSB-AD-U, TSB-AD univariate, TSB-AD-U-Eval, TSB-AD-U-Tuning
- Domain
- mixed
- License
- Apache-2.0 for the preprocessing/curation; each source dataset keeps its own licence
- Test items from
- 2024-11-20
Evidence
“[Nov. 20, 2024]”
raw.githubusercontent.com · readme · News · retrieved 2026-10-15 “is online and release TSB-AD-U/M dataset.”
raw.githubusercontent.com · readme · News · retrieved 2026-10-15 - Homepage
- thedatumorg.github.io
Related datasets
-
subset of UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021)
TSB-AD's 'UCR' is the UCR anomaly archive (ref. [106] = Wu & Keogh). The TSB-AD-U file list has 228 UCR series (Table 2: 250 collected); TSB-AD's training prefix may differ from the UCR file-name prefix.
Evidence
“UCR [106] is a collection of univariate time series of multiple domains including air temperature, arterial blood pressure, ABP, astronomy, EPG, ECG, gait, power demand, respiration, walking accelerator.”
proceedings.neurips.cc · paper · Appendix B.1.1 · retrieved 2026-10-15 “R. Wu and E. J. Keogh. Current time series anomaly detection benchmarks are flawed and are creating”
proceedings.neurips.cc · paper · References [106] · retrieved 2026-10-15 “303_UCR_id_1_Medical_tr_2499_1st_8000.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15 -
subset of Numenta Anomaly Benchmark (NAB)
28 NAB series in the TSB-AD-U file list (NAB has 58 files).
Evidence
“NAB [6] is composed of labeled real-world and artificial time series including AWS server metrics, online advertisement clicking rates, real-time traffic data, and a collection of Twitter mentions of large publicly-traded companies.”
proceedings.neurips.cc · paper · Appendix B.1.1 · retrieved 2026-10-15 “001_NAB_id_1_Facility_tr_1007_1st_2014.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15 -
subset of Yahoo S5 Labeled Anomaly Detection Dataset (Yahoo Webscope S5)
259 YAHOO series in the TSB-AD-U file list (Yahoo S5 has 367); the Eval set samples 8 series from each of the four Yahoo subsets.
Evidence
“YAHOO [51] is a dataset published by Yahoo labs consisting of real and synthetic time series based on the real production traffic to some of the Yahoo production systems.”
proceedings.neurips.cc · paper · Appendix B.1.1 · retrieved 2026-10-15 “551_YAHOO_id_1_Synthetic_tr_500_1st_893.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15 -
derived from Server Machine Dataset (SMD)
Univariate channels of SMD (38 series in the file list).
Evidence
“we implement a transformation strategy that converts multivariate time series into univariate formats by treating each channel as an independent series.”
proceedings.neurips.cc · paper · Section 4.1.1 · retrieved 2026-10-15 “SMD (U) [97]”
proceedings.neurips.cc · paper · Table 2 · retrieved 2026-10-15 “178_SMD_id_1_Facility_tr_6000_1st_10609.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15 -
derived from Mars Science Laboratory (MSL) telemetry anomaly dataset
Univariate channels of MSL (9 series in the file list).
Evidence
“we implement a transformation strategy that converts multivariate time series into univariate formats by treating each channel as an independent series.”
proceedings.neurips.cc · paper · Section 4.1.1 · retrieved 2026-10-15 “MSL (U) [43]”
proceedings.neurips.cc · paper · Table 2 · retrieved 2026-10-15 “140_MSL_id_1_Sensor_tr_530_1st_630.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15 -
derived from Soil Moisture Active Passive (SMAP) telemetry anomaly dataset
Univariate channels of SMAP (19 series in the file list).
Evidence
“we implement a transformation strategy that converts multivariate time series into univariate formats by treating each channel as an independent series.”
proceedings.neurips.cc · paper · Section 4.1.1 · retrieved 2026-10-15 “SMAP (U) [43]”
proceedings.neurips.cc · paper · Table 2 · retrieved 2026-10-15 “531_SMAP_id_1_Sensor_tr_1811_1st_4510.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15 -
derived from Secure Water Treatment (SWaT)
One univariate SWaT channel series in the file list.
Evidence
“we implement a transformation strategy that converts multivariate time series into univariate formats by treating each channel as an independent series.”
proceedings.neurips.cc · paper · Section 4.1.1 · retrieved 2026-10-15 “SWaT (U) [62]”
proceedings.neurips.cc · paper · Table 2 · retrieved 2026-10-15 “550_SWaT_id_1_Sensor_tr_43700_1st_43800.csv”
raw.githubusercontent.com · code · TSB-AD-U file list · retrieved 2026-10-15
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) | Test data seen | a contamination analysis found test examples in the pre-training data (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 | related dataset UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021) was used (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 | the authors state the dataset was excluded from training |
| 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 | in fine-tuning data: related dataset Server Machine Dataset (SMD) was used | |
| Time-series anomaly detection | UniTS | Not seen | absent from the complete list of training data |
Notes
published_from = README news item announcing the release of TSB-AD-U/M (the paper was reviewed on OpenReview earlier in 2024; the data download is https://www.thedatum.org/datasets/TSB-AD-U.zip). The public zip redistributes curated series of the source datasets, including SWaT, whose official copy requires a request form. File-list counts: 870 series in TSB-AD-U.csv, 822 in TSB-AD-U-Eva-Full.csv, 349 in TSB-AD-U-Eva.csv, 47 in TSB-AD-U-Tuning.csv (paper: 350 Eval / 48 Tuning). Other TSB-AD-U sources (IOPS, MGAB, WSD, SED, TODS, NEK, Stock, Power, Daphnet, CATSv2, LTDB, TAO, Exathlon, MITDB, SVDB, OPPORTUNITY) have no registry id yet.
Sources
- The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark (Liu & Paparrizos, NeurIPS 2024 Datasets and Benchmarks Track) · paper · retrieved 2026-10-15
- TSB-AD GitHub README · readme · retrieved 2026-10-15
- TSB-AD project homepage (dataset summary and licences) · website · retrieved 2026-10-15
- TSB-AD-U file list · code · retrieved 2026-10-15
- TSB-AD benchmark runner (Run_Detector_U.py) · code · retrieved 2026-10-15