I have a time series with both continuous and categorical features, and I want to do a prediction task.
I will elaborate:
The data is composed of 100Hz sampling of some voltages, kind of like an ecg signal, and of some categorical features such as "green", "na" and so on.
In total, the number of features can reach 300, of which most are continuous.
The prediction should take in a chunk of frames and predict a categorical variable for this chunk of frames.
I want to create a deep learning model that can handle both categorical and continuous features.
Best I can think of is two separate losses, like MSE and cross entropy, and a hyperparameter to tune between them, kind of like regularization.
Best I could find on this subject was this, with an answer from 2015.
I wonder if something better was invented, since then, or maybe just someone here knows something better.