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Models/M-RNN

M-RNN

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Time Series3 results

ImputationonUCI localization data
MAE (10% missing)· 2017-11-23
0.248
best: 0.219 (BRITS)
SOTA
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks arXiv:1711.08742
ImputationonPhysioNet Challenge 2012
MAE (10% of data as GT)· 2017-11-23
0.451
best: 0.186 (SAITS)
SOTA
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks arXiv:1711.08742
ImputationonBeijing Multi-Site Air-Quality Dataset
MAE (PM2.5)· 2017-11-23
14.24
best: 10.51 (GRIN)
SOTA
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks arXiv:1711.08742

Methodology3 results

Feature EngineeringonUCI localization data
MAE (10% missing)· 2017-11-23
0.248
best: 0.219 (BRITS)
SOTA
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks arXiv:1711.08742
Feature EngineeringonPhysioNet Challenge 2012
MAE (10% of data as GT)· 2017-11-23
0.451
best: 0.186 (SAITS)
SOTA
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks arXiv:1711.08742
Feature EngineeringonBeijing Multi-Site Air-Quality Dataset
MAE (PM2.5)· 2017-11-23
14.24
best: 10.51 (GRIN)
SOTA
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks arXiv:1711.08742