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Multivariate Time Series Forecasting On Ushcn
Metrics
MSE
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| NeuralODE-VAE | 0.96 | Neural Ordinary Differential Equations | |
| Sequential VAE | 0.83 | Structured Inference Networks for Nonlinear State Space Models | |
| NeuralODE-VAE-Mask | 0.83 | Neural Ordinary Differential Equations | |
| T-LSTM | 0.59 | Patient Subtyping via Time-Aware LSTM Networks | - |
| BRITS | 0.53 | BRITS: Bidirectional Recurrent Imputation for Time Series | |
| GRU-ODE-Bayes | 0.43 | GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series | |
| GraFITi | 0.27 | Forecasting Irregularly Sampled Time Series using Graphs | |
| FLD | 0.258 | Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting |
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