Time series / Dataset
Soil Moisture Active Passive (SMAP) telemetry anomaly dataset
Real telemetry of NASA's Soil Moisture Active Passive satellite released by NASA JPL with Telemanom (Hundman et al., KDD 2018): 55 anonymised telemetry channels, each with one telemetry value plus one-hot encoded command information (25 dimensions in the OmniAnomaly table), 69 expert-labelled anomaly sequences (from Incident Surprise, Anomaly reports). Pre-split train and test sets per channel; labels refer to the test set; values are pre-scaled to (-1,1) using the test-set min/max and timestamps are anonymised. OmniAnomaly statistics: 135,183 training and 427,617 test points, anomaly ratio 13.13%.
- Also known as
- SMAP, Soil Moisture Active Passive, NASA SMAP, SMAP satellite telemetry
- Domain
- spacecraft telemetry
- Test items from
- 2018-06-01
Evidence
“Open-source repository and data to be ready upon publication.”
arxiv.org · paper · footnote 1 · retrieved 2026-10-15 “"created_at":"2018-06-01T20:33:20Z"”
api.github.com · code · GitHub API: repository creation date · 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]; 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 | 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
Released in the same package as MSL (relation recorded on msl). published_from = creation date of the Telemanom repository; arXiv v1 (2018-02-13) said data would be released upon publication. No data licence stated in the README (TSB-AD homepage lists 'Caltech').
Sources
- Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding (Hundman et al., arXiv v3) · paper · retrieved 2026-10-15
- Telemanom README (data description, statistics) · readme · retrieved 2026-10-15
- GitHub API metadata of khundman/telemanom · code · retrieved 2026-10-15
- OmniAnomaly README (SMAP statistics as used in later multivariate benchmarks) · readme · 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