Contamination Map Methodology

Time series / Dataset

UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021)

250 univariate time series, each designed to contain exactly one anomaly, introduced by Wu & Keogh. Each file name encodes the split: the first N points (e.g. '2500' in UCR_Anomaly_BIDMC1_2500_5400_5600) are intended as training data and the last two numbers give the location of the single anomaly in the remainder. Anomalies are either natural (confirmed out-of-band) or artificially introduced; TSB-AD notes that most are introduced artificially. Released after the SIGKDD 2021 time-series anomaly detection contest (KDD Cup 2021, hosted by Hexagon ML), which used 200 series split into train and test phases.

Also known as
UCR Anomaly Archive, UCR Time Series Anomaly Archive, Hexagon ML/UCR Time Series Anomaly Archive, UCR_Anomaly, KDD21, KDD Cup 2021 anomaly detection, UCR_TimeSeriesAnomalyDatasets2021, Multi-dataset Time Series Anomaly Detection (KDD Cup 2021)
Domain
mixed (medicine, sports, entomology, industry, space science, robotics)
Test items from
2021-08-14
Evidence
“Update (Aug. 14, 2021): The Hexagon ML/UCR Time Series Anomaly Archive is now available”
wu.renjie.im · website · retrieved 2026-10-15
“We will publicly release the UCR Time Series Anomaly Archive once our anomaly detection contest is over.”
wu.renjie.im · website · retrieved 2026-10-15
Homepage
www.cs.ucr.edu

Exposure by model

BenchmarkModelLevelReason
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) Related data seen 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]; the authors state this dataset was excluded
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 Not seen the authors state the dataset was excluded from training
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 the archive (with labels) was announced as available on the authors' supporting page. Earlier: the arXiv paper announcing the archive appeared 2020-09-29 (v1) and the contest went live 2021-03-15 with 200 series (labels of the test phase withheld); whether all 200 contest series are in the 250-series archive is not stated. The count of 250 was established by listing the official zip (not quoted from a document). No licence stated.

Sources