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Time Series Classification On Physionet

Metrics

AUPRC

Results

Performance results of various models on this benchmark

Paper Title
GRU-D - APC (n = 0)53.3As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
GRU-D [12]53.7As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
GRU-D - APC (n = 1)55.1As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
IP-Nets-Set Functions for Time Series
ODE-RNN-Latent ODEs for Irregularly-Sampled Time Series
Phased-LSTM-Set Functions for Time Series
Latent ODE + Poisson-Latent ODEs for Irregularly-Sampled Time Series
GRU-APC (n = 0)50.4As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
GRU-D [4]-As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
SeFT-Attn-Set Functions for Time Series
GRU-Simple-Set Functions for Time Series
BRITS [4]-As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
Transformer-Set Functions for Time Series
mTAND-Full-Multi-Time Attention Networks for Irregularly Sampled Time Series
GRU-D-Recurrent Neural Networks for Multivariate Time Series with Missing Values
GRU-Forward52As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
Latent ODE (ODE enc-Latent ODEs for Irregularly-Sampled Time Series
GRU-Simple53.8As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
IP-NETS-Interpolation-Prediction Networks for Irregularly Sampled Time Series
RNN ∆t--
0 of 28 row(s) selected.
Time Series Classification On Physionet | SOTA | HyperAI