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Time Series Classification On Physionet

評価指標

AUPRC

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
AUPRC
Paper TitleRepository
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---
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Time Series Classification On Physionet | SOTA | HyperAI超神経