Contamination Map Methodology

Time series / Dataset

Server Machine Dataset (SMD)

5-week-long multivariate dataset of 28 machines (entities machine-<group>-<index>, 3 groups) from a large Internet company, 38 dimensions, released with OmniAnomaly (Su et al., KDD 2019). Each machine's series is split into two halves of equal length: train (former half, no labels) and test (latter half) with point labels (test_label) and the dimensions responsible for each anomaly (interpretation_label). 708,405 training and 708,420 test points; anomaly ratio 4.16%. Machines should be trained and tested separately.

Also known as
SMD, Server Machine Dataset, ServerMachineDataset, OmniAnomaly SMD, OMNI
Domain
IT operations (server monitoring)
License
MIT (OmniAnomaly repository licence)
Test items from
2019-02-02
Evidence
“"created_at":"2019-02-02T11:58:29Z"”
api.github.com · code · GitHub API: repository creation date · retrieved 2026-10-15
“SMD (Server Machine Dataset) is in folder `ServerMachineDataset`.”
raw.githubusercontent.com · readme · Get data · retrieved 2026-10-15
Homepage
github.com

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 Test data seen its test split was used for training (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 Training data seen in fine-tuning data: only the training split was used, not the benchmark's test data
Time-series anomaly detection UniTS Not seen absent from the complete list of training data

Notes

published_from = creation date of the OmniAnomaly GitHub repository, whose first commit (2019-02-02, 'init') already contains ServerMachineDataset; the repository may have become public later, at the latest with the KDD 2019 paper (August 2019). Wu & Keogh call it the OMNI dataset (Pei's Lab) and quote file paths such as OmniAnomaly/ServerMachineDataset/test/machine-3-11.txt.

Sources