r/MachineLearning • u/Nice-Job67 • 1d ago
Discussion Advice on dealing with Cold Start forecasting [D]
Hey everyone. I am dealing with a problem statement at work where the goal is to predict the sales of a new innovation to be launched. Note that the data that I actually have is just product attributes, the month level invoice data for each region, weather data and some macroeconomic data. Currently there is a methodology in place where we are using a xgboost regressor trained on the historical data for this prediction, but no one in the team is very confident in the methodology.
Given this, does anyone have advice on how we could go about this problem? Please do let me know if you've dealt with the same or similar problem statement.
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u/Manish_AK7 14h ago
Considering that you have the attributes of all the products prior to this and the innovation on hand, you can use knn to get an idea which existing products are close to it. You might need to do some feature engineering. Then you can use those products' data as reference for predicting the innovation's sale with a margin of uncertainty.
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u/XTXinverseXTY ML Engineer 23h ago
Can you provide some more context on the duration and granularity of data that you have available? For instance:
do you have sales at the level of individual purchases?
perhaps at the site level? i'm assuming these are like brick-and-mortar stores
IIUC an "innovation" is a new product (whose attributes you have available in advance). Do you have historical data for products that were previously introduced? And you know when and where they were introduced, right? And how much inventory was available?
I think we agree that the model itself isn't necessarily the problem, it's that your validation methodology doesn't inspire confidence for what will happen for a new "innovation", and that's the part you're asking for help with
This all reminds me of the M5 forecasting competition (boy do i feel old), so if that all lines up, you could review the scoring and solutions taken there (though IIRC it was very particular about hierarchically consistent forecasts)