Forecast Accuracy Calculator

Does your demand forecast generate over-stock or out of stock and you don't know how bad it really is? This calculator evaluates your forecast with 10 professional metrics (MAPE, WMAPE, MASE, Bias...), tells you if your model adds value vs. doing nothing, and quantifies the financial impact of your errors.

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What is the Acuraccy forecast calculator?

It is a free web error calculator for Demand forecast. It allows us to evaluate the accuracy of a forecasting model with 10 professional metrics based on historical data of real vs. forecast demand.

Data Entry Modes

The tool offers two methods for entering data:

  1. Manual entry: A row-by-row table is completed with 4 fields:
    • Date (optional, free text)
    • SKU (optional, product identifier)
    • Royal (required, actual demand in units)
    • Forecast (required, predicted value)
    • Unlimited rows can be added with the button “+ Add row” and delete individual rows with the trash can icon.
    • There is a button “Clean everything” to reset the table.
  1. Upload file: It allows you to load an Excel with the data directly (without the need for manual entry).

Reliability notice: The tool notifies you in real time how many periods you have entered and recommends having at least 12 periods for reliable results.

The 10 metrics it calculates

With each calculation, the tool provides the following indicators, each with its contextual description and a colored traffic light (🟢 green/🟡 yellow/🔴 red):

# Métrica Descripción Interpretación
1 Forecast Accuracy % de la demanda que aciertas Se calcula como 100% – WMAPE
2 WMAPE Error porcentual ponderado por volumen La métrica recomendada; da más peso a periodos de mayor demanda
3 MAPE Error porcentual promedio por periodo Sensible a SKUs de bajo volumen
4 MASE Error escalado vs. modelo naive < 1 = agrega valor; > 1 = peor que no hacer nada; < 0.8 = agrega valor real
5 MAE Error absoluto promedio en unidades Cuántas unidades te equivocas en promedio
6 RMSE Raíz del error cuadrático medio Penaliza más los errores grandes
7 SMAPE Versión simétrica del MAPE Rango 0–200%; evita asimetrías del MAPE
8 MAE Naive Error si repitieras la venta del periodo anterior Línea base de referencia
9 Bias Sesgo del pronóstico (positivo o negativo) Negativo = sobre-pronostica; Positivo = sub-pronostica
10 Tracking Signal Detector acumulado de sesgo Fuera de ±4 = alerta de sesgo sistemático

Visual summary panel

In addition to the detail of each metric, the tool shows a summary panel with:

  • 6 key metrics on small cards: WMAPE, MAPE, MASE, Bias, Accuracy and Tracking Signal.
  • Each value has a colored traffic light depending on its level of performance.

Automatic diagnostics

One of the most relevant functions is the automatic diagnosis that interprets the results without the user needing to know how to read the metrics:

  • Compare the MASE against the naive model and issue a verdict:
    • 🔴 ALERT: “Your model is WORSE than simply repeating the sale from the previous period. Review your method.”
    • 🟢 POSITIVE: “Your MASE is 0.23 (<0.8). Your model adds real value vs. a naive forecast. Good work.”

FAQs

What is Forecast Accuracy and how is it calculated?
Forecast Accuracy measures what percentage of demand you are right with your forecast. It is calculated as 100% − WMAPE. An accuracy of 80% means that your forecast is correct for 8 out of 10 demand units.
What is MASE and why is it important?
The MASE (Mean Absolute Scaled Error) compares your forecast against a naive model (repeat the previous sale). If MASE > 1, your model is worse than doing nothing. If MASE < 0.8, your model adds real value.
What is the difference between MAPE and WMAPE?
MAPE averages the percentage error for each period equally, causing low-volume SKUs to distort the result. WMAPE weighs by real volume, giving more weight to periods with the highest demand. WMAPE is the recommended metric.

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