Stephen Witkowski02/05/2023, 2:34 PM
Farzad E02/06/2023, 7:08 PM
Mariana Menchero02/06/2023, 7:17 PM
Stephen Witkowski02/08/2023, 4:44 PM
My recommendation is to try to forecast at a higher level of granularity where you have more data. For instance, think of clustering some products together to get more available data for forecasting.While I expect this could correct the flaw in assumption, this wouldn’t meet the requirements of the project, since we need to forecast individual items not a category of items.
With this approach, you don’t need to remove discontinued items or categories. Just make sure their forecast is zero before doing the splashing.When you use the word “splashing”, are you referring to distributing a forecast down a hierarchy based on proportion? I couldn’t find that phrase in the paper and just wanted to make sure I understand. Repeating it back so I’m clear. In this scenario, records wouldn’t be padded and forecasts would only be produced for an item when data is available. However, once they are converted to a distribution, they would be padded to facilitate the “splashing”. Meaning, there would always be a bottom level proportion for each base level category, however items or categories which either (a) have not yet launched or (b) have been discontinued would be given a fixed 0% for the distribution.
Farzad E02/08/2023, 5:00 PM
Mariana Menchero02/09/2023, 8:54 PM