Soil Moisture Active Passive
Soil Moisture Active Passive (SMAP) dataset is a dataset of soil samples and telemetry information using the Mars rover by NASA. Originally published in https://arxiv.org/abs/1802.04431 and used for the unsupervised anomaly detection task in time series data. Later it was used in many popular anomaly detection methods and benchmarks that distribute it in their repositories e.g., https://github.com/OpsPAI/MTAD
Variants: SMAP
This dataset is used in 2 benchmarks:
| Task | Model | Paper | Date |
|---|---|---|---|
| Unsupervised Anomaly Detection | DFM (flow matching) | DFM: Interpolant-free Dual Flow Matching | 2024-10-11 |
| Unsupervised Anomaly Detection | ContextFlow++ (Glow-based) | ContextFlow++: Generalist-Specialist Flow-based Generative Models … | 2024-06-02 |
| Time Series Anomaly Detection | CARLA | CARLA: Self-supervised Contrastive Representation Learning … | 2023-08-18 |
| Unsupervised Anomaly Detection | TranAd | TranAD: Deep Transformer Networks for … | 2022-01-18 |
| Unsupervised Anomaly Detection | CAE-M | Unsupervised Deep Anomaly Detection for … | 2021-07-27 |
| Unsupervised Anomaly Detection | GDN | Graph Neural Network-Based Anomaly Detection … | 2021-06-13 |
| Unsupervised Anomaly Detection | MTAD-GAT | Multivariate Time-series Anomaly Detection via … | 2020-09-04 |
| Unsupervised Anomaly Detection | Glow | Glow: Generative Flow with Invertible … | 2018-07-09 |
Recent papers with results on this dataset: