Time Series Classification On
Metrics
Accuracy
NLL
Results
Performance results of various models on this benchmark
Model Name | Accuracy | NLL | Paper Title | Repository |
|---|---|---|---|---|
| GP-Sig-LSTM | 0.991 | 0.031 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| SNLST | 0.957 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-LSTM | 0.233 | 2.506 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-KConv1D | 0.941 | 0.409 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-Sig | 0.979 | 0.108 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| MALSTM-FCN | 1 | - | Multivariate LSTM-FCNs for Time Series Classification | - |
| GP-Sig-GRU | 0.925 | 0.258 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| FCN-SNLST | 0.994 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-GRU | 0.114 | 3.523 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
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