Seasonal ARIMA Forecasting of Ozone Concentration

Applied forecasting case where the value lies in following a full process: preparing the series, identifying temporal structure, comparing models, validating residuals, and presenting forecasts with uncertainty.

Many business decisions depend on anticipating demand, activity, traffic, sales, or operational indicators. This case uses an environmental series as an academic context, but the logic is transferable to KPI, demand, or commercial-activity planning.

Individual academic project. I prepared the series, ran the Box-Jenkins workflow, compared SARIMA models in Stata, validated residuals, and generated dynamic forecasts with intervals.

1. 01Visualise the series and decide to work in logs.
2. 02Apply seasonal and regular differencing.
3. 03Identify candidate models with ACF/PACF.
4. 04Estimate SARIMA alternatives in Stata.
5. 05Select the model using AIC/BIC and residual diagnostics.
6. 06Generate dynamic forecasts and 95% intervals.

• Academic context and environmental series.
• Does not include exogenous variables.
• Forecast quality depends on temporal stability and model assumptions.

• Compare with models using exogenous variables.
• Evaluate out-of-sample errors.
• Create a forecast dashboard and alerts.