Sign up to take part
Registered users can ask their own questions, contribute to discussions, and be part of the Community!
Registered users can ask their own questions, contribute to discussions, and be part of the Community!
I would like to understand how the Mean absolute scaled error is implemented in Dataiku. For time series, does it take into account whether there is a seasonality in the data, i.e. are different formules applied for seasonal and non-seasonal data as explained here: Mean absolute scaled error - Wikipedia ?
In general, it would be good to get a proper documentation on all Metrics available in Visual Modelling.
Hi @klaudia_lubian ,
I believe you may have received a response to this over a support ticket already:
MASE is the mean absolute error of the test horizon divided by the mean absolute error of a trivial identity model applied to all the data (for all time steps in the training + test data, you compute the absolute difference between the current time step and the time step one horizon ago) => this is why it is not 1.
Division by 0 always leads to a NaN metric.
Thanks,