forecasting

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

Prophet treats forecasting as curve-fitting trend plus seasonality plus holidays, rather than 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 combines demand forecasting, optimization, and real-time serving, under fairness and trust constraints. The signal is the predict-then-optimize structure plus guardrails against perverse outcomes. Here is the design.

Forecasting a single series is textbook work; forecasting a million SKU-stores that have to reconcile, where stockouts cost more than overstock, is what the interview actually tests. The strong answer is global, hierarchical, and cost-aware.