Two features are measured at different times but belong to the same target. In which conditions or form these features can be modeled together? Or they shouldn't be used in the same model but modeled separately? E.g. I am trying to identify users by looking at their mouse dynamics data and keyboard dynamics data, however, these two are measured independently but they belong to the same person (I have this scenario for many users). To explain it better, imagine it like I gave a text to users to repeatedly write them using the keyboard (and I recorded their keyboard usage) and similarly I gave them a different task to use mouse repeatedly (and I recorded that too). The sample size is also different for each feature.
From the example I assume that an instance corresponds to a user, and you have both full sequences of the mouse and keyboard as features for predicting the user. I can think of two options for using these features in the same model:
- With feature engineering, find a way to represent both sequences as a fixed array of features. For example you might have features such as average typing speed average mouse speed, number of mouse movements, number of times each key is pressed, etc.
- Similar idea but in a more DL approach: find a way to represent both sequences as embeddings (there are methods for word embeddings, sentence embeddings, graph embeddings...)
In my opinion the main issue is the variable length of the sequences, not the fact that they are not aligned (the alignment would matter if the target variable was for one element in the sequence).