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: 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.