Learning objectives
- • Explain stationarity, trend, seasonality, lag features, and rolling windows
- • Use time-based validation instead of random splits
- • Choose metrics like MAE, RMSE, MAPE, pinball loss, and service-level error
Traditional ML
Time Series Forecasting
Add forecasting coverage for trend, seasonality, leakage, backtesting, hierarchical forecasts, and anomaly-aware evaluation.
Recommended on day 1995 minutesIntermediate
Interview prompts
- • Why is random cross-validation wrong for most forecasting problems?
- • How do you evaluate forecasts for intermittent demand?
Prerequisites