Problem

Planning inventory, staffing or budget on a single guess is risky when demand swings seasonally.

Solution

Train on your historical data and forecast the future with a central point estimate plus lower/upper confidence bounds.

Example

Forecast total impressions with a 95-score history; the model reproduces seasonal peaks and quantifies uncertainty.

Problem

Simple forecasts that only use past values miss the real drivers of demand like spend, launches or holidays.

Solution

Feed external variables (marketing spend, campaign dates, holidays, traffic) alongside history for context-aware predictions.

Example

Combine total_impressions_history with xreg forecasts to predict a March peak of ~25M within a 17M–27M range.

Problem

A single number hides risk; teams over- or under-provision because they do not see the range.

Solution

Use the lower and upper bounds to plan for the likely worst and best case, not just the average.

Example

Size infrastructure and staffing to the confidence interval instead of a single point estimate.