Multivariate Time Series Forecasting On Mimic
Metrics
MSE
Results
Performance results of various models on this benchmark
Model Name | MSE | Paper Title | Repository |
|---|---|---|---|
| FLD | 0.444 ± 0.027 | Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting | |
| GRU-ODE-Bayes | 0.480 ± 0.010 | GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series | - |
| Neural Flows | 0.490 ± 0.004 | Neural Flows: Efficient Alternative to Neural ODEs | |
| T-LSTM | 0.790 ± 0.060 | Patient Subtyping via Time-Aware LSTM Networks | - |
| GraFITi | 0.396 ± 0.030 | Forecasting Irregularly Sampled Time Series using Graphs |
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