1. Problem

Not every forecasting problem needs a custom model stack. Sometimes the real challenge is building a reliable workflow around a strong baseline and being honest about what it can and cannot capture.

2. Approach

This workflow used Prophet as a practical forecasting baseline for messy game-sales data, with enough preprocessing and segmentation to make the outputs useful rather than decorative.

  • Clean and aggregate the data into a forecastable surface.
  • Use Prophet where seasonality and trend structure justify it.
  • Inspect misses to understand what the model is blind to.

3. Evidence

Individual Prophet forecast
The forecast looked strong where the structure was stable and much weaker where promotions or event effects were missing from the model.
Aggregated Prophet forecast versus actuals
The aggregated view made it easier to discuss when the baseline was good enough and when the business needed richer features.

4. Outcome

The project showed how a relatively lightweight forecasting stack can still provide decision value when the workflow is disciplined and the limitations are explicit.

5. Tech stack

  • Prophet for the baseline forecasting model
  • Pandas for preparation and aggregation
  • Visualization for model-review conversations

6. Useful links

7. Related reading

8. Call to action

If you need a forecasting baseline that is quick to ship and easy to explain before investing in something heavier, I can help set up the workflow and evaluation pattern.