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Dynamic Number of Time Series Forecasted in Watson Studio SPSS Modeler Flow
There is 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). This can happen (but is not limited to) when using MDX to import data from IBM Planning Analytics.
Often there are hierarchical dimensions where one would like to forecast for all timeseries in a (sub)hierarchy. For example when forecasting monthly for all accounts in an account group.
There can be hundreds of timeseries and the number might change between runs. The change can be because a series added/deleted in the hierarchy or because the user wants to run a different subset of series (e.g. if erroneous values were detected in the historical data for those).
It is very time-consuming and frustrating to manually verify all target fields every time the script is run. Since this can be solved with a trivial for-loop in Python, Modeler Flow should also have some way to tackle this.
There seems to be two methods (at least) that would work: 1) Each column (excluding the time column) contains one timeseries to be used as Target. 2) Each column (excluding the time column and the value column) corresponds to one dimension. All unique combinations of dimension column values define one timeseries to be used as Target.
All of this can currently be done in Watson Studio if using Jupyter notebooks. However, they are more of an exploration tool and it doesn't look so great marketing-wise that a fancy tool like Watson Studio is just a place to put your open source scripts. Modeler Flow would be more sexy, easier to explain to end users, and more structured.
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