Time series as one of several inputs to ML Model I have what seems to be a relatively simple question that I haven't been able to find a satisfactory answer to despite searching for quite a long time:
The classic way to analyze time series data seems to be some flavor of RNN. However, most examples of this I've seen have used an input of single time series to RNNs. My application calls for a slightly more complex architecture - the inputs for a given inference would be a combination of several short time series, as well as several individual parameters. In visual form, this might look something like the following:

There are two main questions here:

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*How do I handle the fact that there are two different types of time series here - they can't easily just be stacked and fed into the same RNN (or can they?)

*How do I handle the fact that there are also parameters involved which don't change over the course of a time series? Do I just tack on each of these parameters to each timestep of the time series, or do I do something else?

Right now I'm considering just trying to do some hacky Concatenate layer stuff, but it seems like a common situation like this would warrant a more elegant solution.
Some related questions I've found: this one, which mentions pretty much my exact question but never got an answer, this one, which has a variable amount of input time series and wants to do some specific operations on them, and this tutorial which talks about concatenation.

If all your time series are the same length, cover the same time period and the time steps coincide then your proposed solution to stack them and feed through a single RNN should work. It would make sense to do this if your time series are related and you want to extract joint features. Otherwise you can feed each time series though a separate RNN and concatenate the output before feeding this through the dense layers.
For the static features, concatenating these with the RNN output before the dense layers seems a good solution to me. You could first feed these through some separate dense layers if you want to extract some low-level static features.
While tacking these features on to each time step is feasible, to me it doesn't make a lot of sense. Replicating that feature across multiple time steps could imply relationships that don't exist and/or lead to the trained model putting too much weight on it.
