Different features in a time series have different observation times

[cherrywinaikosol]

Hi, my time series have many features and each of them is observed at different times. How can I use latent-ode here to learn from such data? It seems the current collate_fn can be used for time series with different observation times but in each time, it assumes all the features are observed (but in my case, some features are missing while others are observed in one time series).

Can I just use the predicted values of unobserved features from the ode forward (i.e. x(t) = ODEsolver(neural_net, y0, time at the next observation) and set x_unobserved = x[index of unobserved features] ), and update the state by GRU using x(t) = [x_observe at t, x_unobserved at t] as the input to the GRU.

Please suggest.

Thank you.