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Time Series Classification On Wafer

Métriques

Accuracy

NLL

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	NLL	Paper Title	Repository
GP-LSTM	0.966	0.105	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
MALSTM-FCN	0.99	-	Multivariate LSTM-FCNs for Time Series Classification	-
SNLST	0.981	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections	-
GP-GRU	0.994	0.029	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-Sig-GRU	0.978	0.081	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-Sig	0.965	0.105	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-KConv1D	0.984	0.085	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
FCN-SNLST	0.989	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections	-
GP-Sig-LSTM	0.988	0.048	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
R_DST_Ensemble	0.9999513303049968	-	Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets	-

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Time Series Classification On Wafer | SOTA | HyperAI