Revenue ForecastingRevsure AIDec 2024 – PresentProduction
Time-Decay Forecast Adjustment
Dynamic adjustment layer that re-weights prior EOQ history by recency, capturing momentum and velocity signals closer to quarter end.
Production
Deployed
The Problem
Static average-based priors weighted all historical quarters equally, ignoring recent pipeline momentum or slowdown signals that are most predictive close to quarter end.
What Was Built
Built a time-decay adjustment layer that exponentially down-weights older EOQ observations, emphasizing recent quarters. Incorporates momentum, velocity, and capacity features. Deployed as a production adjustment layer on top of the base XGBoost forecast.
Business Impact
Improved late-quarter forecast accuracy by capturing recency signals, deployed to production as part of the Revenue Forecasting Platform.
Tech Stack
PythonXGBoostscikit-learnBigQuery
Domain Tags
Time-DecayBayesian UpdatingRevenue ForecastingMomentum Features
Details
- Role
- Primary Owner
- Status
- Production
- Tier
- Tier 1
- Period
- Dec 2024 – Present
- Employment
- Revsure AI