Time Series Tutorial

Forecast ordered data with explicit calendars, causal features, baselines, rolling evaluation and release checks.

Learning path

• Time-series calendar: distinguish missing periods from measured zeros
• Lag features: prove each input existed at forecast time
• Seasonal naive forecast: establish a baseline before fitting a model
• Rolling-origin backtests: rehearse the forecast as it would have run
• Forecast metrics by horizon: keep errors, zeros and denominators explicit
• Forecast intervals: measure future-outcome coverage by horizon
• Forecast monitoring: separate data delay, demand shift and model failure
• Project: forecast receipt volume with a reproducible rolling backtest

Connected foundations

Use the earlier data and model boundaries as prerequisites.

• Data Engineering Tutorial
• Machine Learning Tutorial

Practice

• Time Series: Projects

Continue into another subject: Causal Inference Tutorial.

Continue into another subject: Streaming Analytics Tutorial.

Continue into another subject: Anomaly Detection Tutorial.