Seasonal repetition as a planning baseline
Seasonal naive forecasting copies the last complete seasonal cycle into the future, making it a reliable baseline when demand, traffic, or volumes repeat predictably. If you have monthly data with a 12-period cycle, the next January forecast equals the last observed January. This baseline helps teams validate whether a model is adding value.
Choosing the season length with real patterns
Pick the season length to match your operational cadence: 7 for day-of-week effects, 12 for month-of-year effects, 24 for hourly daily seasonality, or 52 for weekly year seasonality. The calculator enforces that you have at least one full season of observations, because the method needs a reference cycle to repeat. If you see two repeating cycles, start with the shorter one and compare errors.
Interpreting fitted values and forecast horizon
Fitted values start after the first m periods. For each time index t, the fitted value is the observation from t−m. A horizon of k produces k future points by looping through the last season. This creates forecasts quickly and makes it easy to stress test planning ranges. For example, with m=12 and k=18, you reuse the last 12 months and then repeat the first 6 months.
Accuracy metrics for quick diagnostics
MAE summarizes typical absolute deviation in the same unit as your data. RMSE penalizes large misses more heavily, which is useful when spikes are costly. MAPE expresses average absolute percentage error, but can be unstable when actual values are near zero. Use the metrics together to see stability and risk,.
When the baseline works and when it fails
The baseline works best with stable seasonality and limited trend. It can underperform when growth is strong, when there is structural change, or when seasonality shifts over time. If errors increase steadily or the chart shows systematic bias, consider adding trend methods or using seasonal decomposition approaches. A quick check is whether residuals cluster by month or weekday, indicating a mis-specified season length.
Operational uses: budgeting, staffing, and monitoring
This calculator is practical for budget rollups, staffing schedules, reorder points, and anomaly monitoring. Exporting CSV supports dashboards and audits, while PDF helps quick review in meetings. Treat seasonal naive as the “minimum acceptable” benchmark; any advanced method should beat it on both accuracy and simplicity of explanation. When you deploy a better model, keep this baseline running as a guardrail, and alert when the advanced forecast is worse for several consecutive periods.