Datasets › SKAB
SKAB (Skoltech Anomaly Benchmark)
SKAB is designed for evaluating algorithms for anomaly detection. The benchmark currently includes 30+ datasets plus Python modules for algorithms’ evaluation. Each dataset represents a multivariate time series collected from the sensors installed on the testbed. All instances are labeled for evaluating the results of solving outlier detection and changepoint detection problems.
Benchmarks archive 2025-07-28
All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Change Point Detection | SKAB | LSTMCaps NAB (standard) 27.77 | Hybridization of Capsule and LSTM Networks for... | — | 7 | Compare |
| Outlier Detection | SKAB | LSTMCaps Average F1 0.74 | Hybridization of Capsule and LSTM Networks for... | — | 1 | Compare |
Papers archive 2025-07-28
2 shown of 2 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 3. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data | 0 | 2 | 11 Feb 2022 | not harvested |
| Unsupervised Offline Changepoint Detection Ensembles | 1 | 6 | 9 May 2021 | not harvested |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
GNU General Public License v3.0
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- SKAB
1 variant name, as the archive lists them.
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