Time Series Classification On Netflow
Metriken
Accuracy
NLL
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | NLL | Paper Title | Repository |
|---|---|---|---|---|
| GP-LSTM | 0.928 | 0.251 | 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 | - |
| 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-GRU | 0.926 | 0.194 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| FCN-SNLST | 0.960 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-Sig-GRU | 0.921 | 0.259 | 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 | - |
| SNLST | 0.793 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
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