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Anomaly Detection on MSL

94.08F1DGHL
-20.36169.349239.0668.7708Jan 18, 2022Oct 6, 2022Jun 25, 2023Mar 13, 2024Nov 30, 2024Aug 19, 2025May 8, 2026

Evaluation results

MethodLinks
DGHL2023.0794.08—
Anomaly Transformer2023.0793.59—
TranADTraining data percentage=20%2022.0191.7298.57
THOC2023.0789.69—
Base ModelModel Architecture=COUTA2026.0589.699.2
GDNTraining data percentage=20%2022.0189.5994.14
FeDaL2025.0889.05—
FFTS2025.0888.42—
Omni Anomaly2023.0787.67—
TimesNet2023.0785.15—
TimesNet2025.0885.15—
MTCN2025.0884.92—
Dlinear2023.0784.88—
DLinear2025.0884.88—
Pyraformer_suffix=true2025.0884.86—
Reformer_suffix=true2025.0884.4—
OmniAnomalyTraining data percentage=20%2022.0184.2497.03
Informer_suffix=true2025.0884.06—
Anomaly2025.0883.31—
GPT4TS2025.0882.45—
MSCREDTraining data percentage=20%2022.0182.3297.97
FedAvg2025.0882.32—
MAD-GANTraining data percentage=20%2022.0181.996.49
USADTraining data percentage=20%2022.0181.996.49
Moment2025.0881.45—
UniTSEvaluation Protocol=FT2025.1081.2—
iTransformerEvaluation Protocol=FT2025.1080.4—
MTAD-GATTraining data percentage=20%2022.0180.2497.82
DAGMMTraining data percentage=20%2022.0180.196.06
PatchTSTEvaluation Protocol=FT2025.1079.9—
Autoformer_suffix=true2025.0879.05—
LightTS2023.0778.95—
LightTS2025.0878.95—
FEDformer_suffix=true2025.0878.57—
FITS2023.0778.12—
Anomaly Trans.2025.1078—
Stationary Transformer2023.0777.5—
NSformer_suffix=true2025.0877.5—
LSTM-NDTTraining data percentage=20%2022.0176.0895.2
UniTSEvaluation Protocol=PMT2025.1075.4—
TsLLM2025.1073.3—
CAE-MTraining data percentage=20%2022.0173.0398.36
OCSVM2023.0770.82—
IForest2023.0766.45—
TimesNetEvaluation Protocol=FT2025.1033.9—
MERLINTraining data percentage=20%2022.0133.4562.81
IMP2023.07-15.96—