Time Series Classification On Pems
Métriques
Accuracy
NLL
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | NLL | Paper Title | Repository |
|---|---|---|---|---|
| GP-Sig | 0.820 | 0.520 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-GRU | 0.769 | 0.784 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-Sig-GRU | 0.775 | 1.100 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| FCN-SNLST | 0.857 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-Sig-LSTM | 0.763 | 0.704 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-LSTM | 0.745 | 1.194 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-KConv1D | 0.794 | 0.537 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| SNLST | 0.747 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
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