- I understand that I can set up a convolutional network for 1-dimensional sequence/time series.
model = Sequential() model.add(Conv1D()) model.add(GlobalMaxPooling1D()) model.add(Dense())
- Let's say I'd like to use "regular" (non-deep-learning) features too in my model, how should I best combine the two at a dense layer?
Concretely, let's assume that, for each row of my dataset, there are 1k points in the time series, along with 100 "regular" features.
- To generalize my question, let's say there are now two kinds of time series plus regular features for each row in my dataset. If I would like to have a separate convolutional block for each time series, how do I combine all three?