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!
Hi Dtaikers!
I want to forecast multiples Time Series using The new Forecast plugin, my time series have different history length, I already interpolated missing values for each one, but I don't want to use extrapolation because it will bias my study, so as a result, I have a dataset with multiple Time Series, identified by store ID, and having different history-length.
For instance: Store x has history from 2011 TO 2015 AND Store y has history from 2014 to 2020 and so on...
I want to know if by activating the Long format and adding identifiers, the Forecasting Horizon will be adjusted for each TS, or I should partition my input dataset and then train partitioned forecasting models?
Thank you!
Hi,
The multiple time series can have different history lengths but the forecasting horizon is the same for all time series, starting after the last day of each time series (in your example, if the forecasting horizon is 1 year, then the models will forecast year 2016 for store x and 2021 for store y).
Hi,
The multiple time series can have different history lengths but the forecasting horizon is the same for all time series, starting after the last day of each time series (in your example, if the forecasting horizon is 1 year, then the models will forecast year 2016 for store x and 2021 for store y).
So I can use the long format directly. Thank you @StanG!