How to forecast a dynamic number of time series in Watson Studio Modeler Flow

In Watson Studio Modeler Flow, how do I forecast time series when the number of series is dynamic?

I have studied tutorials and demos but I have only found methods that require each field's type to be manually specified. This is not feasible when there are hundreds of time series and the exact number of series changes between runs.

I need to be able to run a forecasting job even if the amount of timeseries changes (without going to the modeler to manually select a Type for each series). Since this is a trivial for-loop in Python, I expect that Modeler Flow should have some way to tackle this.

My desired flow (step 2 is the problem):

Import data from an external data source with a Connection.
Create forecasts for each individual series in the data. The number of series may vary between runs. (All the series have the same time values so they could share the time field.)
Export results back to the source system.

Help would be appreciated :)

Solution

I asked the technical team about your question. Their reply is: "Currently Modeler requires specific target field names for Time Series. It is not supported to claim “target = all fields”.

The feature is currently not supported, but you can file a suggestion here: https://ibm-data-and-ai.ideas.ibm.com/?category=6982949146944943308.