forecasting

Time series breaks the usual ML assumptions: data is ordered and correlated, so random cross-validation quietly leaks the future and inflates your score. The signal is decomposition, point-in-time features, and time-aware validation.

Prophet treats forecasting as curve-fitting trend plus seasonality plus holidays, not as a stochastic process. The signal is knowing why that design wins on business data and where it quietly fails. Here is the answer.

Dynamic pricing blends demand forecasting, optimization, and real-time serving, with fairness and trust constraints. The signal is the predict-then-optimize structure plus guardrails against perverse outcomes. Here is the design.

Forecasting one series is a textbook exercise; forecasting a million SKU-stores that must reconcile, with stockouts costing more than overstock, is the real interview. The strong answer goes global, hierarchical, and cost-aware.