GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series

NeurIPS 2019 Edward De BrouwerJaak SimmAdam AranyYves Moreau

Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)-such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential Equations (Chen et al., 2018), and (2) a Bayesian update network that processes the sporadic observations... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Multivariate Time Series Forecasting MIMIC-III GRU-ODE-Bayes MSE 0.48 # 1
NegLL 0.83 # 1
Multivariate Time Series Forecasting USHCN-Daily GRU-ODE-Bayes MSE 0.43 # 1

Methods used in the Paper


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