Time Series Classification On Walkvsrun
Metrics
Accuracy
NLL
Results
Performance results of various models on this benchmark
Model Name | Accuracy | NLL | Paper Title | Repository |
|---|---|---|---|---|
| GP-Sig-GRU | 1.000 | 0.030 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-GRU | 1.000 | 0.028 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-LSTM | 1.000 | 0.048 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| SNLST | 1 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-KConv1D | 1.000 | 0.066 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| MALSTM-FCN | 1 | - | Multivariate LSTM-FCNs for Time Series Classification | - |
| FCN-SNLST | 1 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-Sig-LSTM | 1.000 | 0.030 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-Sig | 1.000 | 0.023 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
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