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.

[wfcgdut]

嗨，我的时间序列有很多特征，每个特征都是在不同的时间观察到的。我如何在此处使用 latent-ode 从这些数据中学习？似乎当前collate_fn可用于具有不同观测时间的时间序列，但在每次中，它都假设观察到所有特征（但在我的情况下，一些特征缺失，而另一些特征在一个时间序列中观察到）。

我是否可以只使用 ode 向前的未观察到特征的预测值（即 x（t） = ODEsolver（neural_net， y0， time at the next observation） 并设置 x_unobserved = x[未观察到特征的索引]），并使用 x（t） = [x_observe at t， x_unobserved at t] 作为 GRU 的输入来更新 GRU 的状态。

请建议。

谢谢。

Hello, may I ask if you have any dependencies to run this code and if there are any files similar to "requirements. txt"