Time Series Forecasting

What Time Series Forecasting is

Time series problems differ from ordinary regression because observations are dependent, trends and seasonality exist, and validation must never use the future to predict the past.

How it works

Classical methods include ARIMA and exponential smoothing; modern practice uses gradient boosting on lag features, or deep and foundation models trained across many series.

Why it matters

Demand, staffing, capacity and finance planning all depend on it, and the evaluation pitfalls are distinctive enough to catch experienced modellers.

Common uses

• →Retail demand planning
• →Energy load forecasting
• →Capacity and staffing
• →Anomaly detection on metrics