Hi! I'm currently working my way into NeuralForecast and have a somewhat basic question I couldn't find any documentation about:
When I'm creating my timeseries, let's say daily sells of a certain product, should I add a y=0 for dates the product hasn't been sold at all or can I just skip the date in the time series?
I know it works with gaps in the time series, but I'm wondering if the result is accurate or if that distorts the model
05/16/2023, 9:54 PM
Generally speaking, if the model sees data that is closer "to the reality" it will perform better. For sales data this normally means that is is appropriate to fill missing dates with 0s given that that is the correct amount of sales for that period.
That being said it is crucial to try to include an "out of stock signal" so the model knows that maybe sales where 0 because there wasn't any stock.
05/17/2023, 8:13 AM
Thank you so much for that answer!
We actually keep selling if we're out of stock and just have longer delivery times. Still would be interesting to see how the longer delivery times effect the sales.
The out if stock information would be a historical exogenous variable I assume?
I actually played with those variables a little bit for another problem but can't get to a result I would expect. We always have a massive spike of sales on Black Friday. If I add the historical exogenous variable with a 1 on Blackfriday and zeros on all dates, I assumed that if I add Blackfriday to the future exogenous variables, I would see a spike in the predictions. But it doesn't seem to have an impact.
Am I missing something?
06/13/2023, 5:02 PM
@Max (Nixtla) Do you have feedback here? Should I include external dates such as black friday as suggested with 0s and 1s?
06/13/2023, 9:40 PM
Sorry for ghosting you @Gerrit Rindermann! Hi @Pascal Schindler.
Yes, out if stock information would be a historical exogenous variable, something like SpecialDate and NotSpecialDate as a dummy. And
you can also encode the "distance" to the event. Something like 3,2,1, for 3, 2 and 1 days before and after the event.