Outlier Detection On Ecg5000
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| VRAE+SVM | 0.9843 | Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection | - |
| GENDIS | 0.94 | GENDIS: GENetic DIscovery of Shapelets | |
| F-t ALSTM-FCN | 0.9496 | LSTM Fully Convolutional Networks for Time Series Classification |
0 of 3 row(s) selected.