I am trying to solve a multimodal regression task. I have two types of time series available.
- Continuous data
- One-hot encoded event data
Both time series are sparsely and irregularly sampled, with continous timestamps. For each point in time, a target label is available. I would like to find a way to create a joined embedding of both data types, ideally regularly sampled, so it can be used as input for a standard RNN-block.
So far, I was not able to find a good approach for this problem.