Why accuracy metrics matter in forecasting workflows
Forecasting is only useful when teams can trust the size, direction, and stability of errors over time. This tool turns paired actual and forecast values into repeatable evidence for planning, inventory, staffing, and budgeting. By summarizing errors across many periods, you can separate random noise from systematic bias and decide whether to adjust models, data inputs, or business assumptions.
Interpreting MAE, RMSE, and percentage measures
MAE reports the typical miss in original units, which helps operational teams translate accuracy into cost or capacity. RMSE increases when a few periods have very large mistakes, so it highlights risk and volatility. MAPE is intuitive as a percent, but it ignores rows where the actual value is zero; sMAPE and WAPE provide alternatives that remain usable across different scales and mixed magnitudes.
Bias diagnostics using ME, CFE, and tracking signal
Accuracy alone can hide directional problems. Mean Error (ME) indicates whether forecasts run high or low on average, while CFE accumulates those errors to show drift. Tracking Signal divides CFE by MAD, giving a standardized indicator of sustained bias. Large positive values typically suggest under-forecasting, and large negative values suggest over-forecasting, prompting review of assumptions and recent demand shifts.
Comparing performance with MASE and seasonal period
MASE scales MAE against a simple seasonal naive benchmark, using a user-chosen period p. When MASE is below one, your approach beats the naive baseline; above one means the baseline is hard to improve on. Setting p to 7 for daily data with weekly seasonality, or 12 for monthly data, makes comparisons fair across products, regions, and time horizons.
Turning results into actions and continuous improvement
Use the row-level table to spot outliers, promotions, stockouts, or one-time events that distort averages. Combine MAE or WAPE with business thresholds to define acceptable error bands per SKU or segment. If RMSE rises while MAE stays steady, prioritize reducing extreme misses. Export CSV for audits and collaboration, and share PDF summaries in review meetings to document decisions and track progress. Recalculate after each model update, and keep the same data window to ensure comparisons remain valid and stable.