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Time Series Classification On Japanesevowels

Metrics

Accuracy

NLL

Results

Performance results of various models on this benchmark

Model Name	Accuracy	NLL	Paper Title	Repository
GP-Sig-LSTM	0.981	0.080	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-Sig	0.982	0.069	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-KConv1D	0.986	0.067	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-LSTM	0.982	0.061	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-Sig-GRU	0.985	0.053	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
GP-GRU	0.986	0.052	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances	-
MALSTM-FCN	0.99	-	Multivariate LSTM-FCNs for Time Series Classification	-
SNLST	0.979	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections	-
FCN-SNLST	0.980	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections	-
ConvTran	0.9891	-	Improving Position Encoding of Transformers for Multivariate Time Series Classification	-

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Time Series Classification On Japanesevowels | SOTA | HyperAI