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Darius Marginean

12/22/2022, 5:22 PM
Hello everyone! I've got a question for you guys. If we want to use 
differences=[]
do we need to do some scaling on data?
Copy code
fcst = MLForecast( 
                 models=models, 
                 freq='W-MON',
                 differences=data['differences']
                )
Copy code
models = [lgb.LGBMRegressor()]
fcst = MLForecast( 
                 models=models, 
                 freq='W-MON',
                 differences=[]
                )
m

Max (Nixtla)

12/22/2022, 5:39 PM
@José Morales, this one is for you.
j

José Morales

12/22/2022, 6:13 PM
Do you mean using the empty list as differences?
d

Darius Marginean

12/22/2022, 6:14 PM
@José Morales yes
I ve noticed that if I left the differences as an empty list the predicted values are the same
j

José Morales

12/22/2022, 6:18 PM
The differences argument is there to specify sequential differences to apply to the series, e.g. [1,12] first subtracts the previous value and then the 12th. If you don't pass anything or pass the empty list it won't take any. What are you trying to accomplish?
d

Darius Marginean

12/23/2022, 10:13 AM
We are working on a weekly timeseries dataset, and didn't quite know what that argument did. We've seen that if we set 
differences = [52]
the output of the predictions wasn't the same on the whole horizon. We've set 
differenences = [52]
as a suggestion from @fede (nixtla) (they/them) in a previous thread to capture the yearly trend & seasonalities.
Mostly I was wondering if there's something else we should do to our data (in case we don't want to use differences) to not predict the same values for the whole horizon
j

José Morales

12/28/2022, 2:35 AM
Maybe a straight line is the best prediction. What does your data look like?
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