Time Series Classification On Netflow

Metriken

Accuracy

NLL

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

			Paper Title
FCN-SNLST	0.960	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
MALSTM-FCN	0.95	-	Multivariate LSTM-FCNs for Time Series Classification
GP-KConv1D	0.945	0.168	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-Sig	0.937	0.189	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-Sig-LSTM	0.931	0.218	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-LSTM	0.928	0.251	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-GRU	0.926	0.194	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-Sig-GRU	0.921	0.259	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
SNLST	0.793	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections

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Time Series Classification On Netflow | SOTA | HyperAI