Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit. (arXiv:2304.07025v1 [stat.ML])

Irregularly measured time series are common in many of the applied settings
in which time series modelling is a key statistical tool, including medicine.
This provides challenges in model choice, often necessitating imputation or
similar strategies. Continuous time autoregressive recurrent neural networks
(CTRNNs) are a deep learning model that account for irregular observations
through incorporating continuous evolution of the hidden states between
observations. This is achieved using a neural ordinary differential equation
(ODE) or neural flow layer. In this manuscript, …

application arxiv challenges continuous deep learning differential equation equation evolution flow forecasting imputation medicine modelling networks neural networks ordinary overview series statistical strategies time series tool