{
    "name": "MAPE Calculator",
    "url": "https://calculators.geekmidst.com/calculators/mean-absolute-percentage-error-calculator",
    "markdown": "https://calculators.geekmidst.com/calculators/mean-absolute-percentage-error-calculator.md",
    "category": "Statistics & probability",
    "description": "Measure forecast accuracy as a percentage: MAPE, symmetric MAPE and weighted WAPE from actual and forecast values, with zero actuals handled explicitly.",
    "summary": "MAPE averages each error as a percentage of the actual value, so forecasts of different-sized items can be compared. WAPE and sMAPE are shown because MAPE misbehaves near zero.",
    "also_called": [
        "Forecast accuracy calculator",
        "Mean absolute percent error calculator"
    ],
    "applies_to": "Worldwide",
    "last_updated": "2026-10-29",
    "computation": "Results are computed in the visitor’s browser on the HTML page. This API describes the calculator; it does not compute results.",
    "inputs": [
        {
            "name": "actual",
            "label": "Actual values",
            "type": "textarea",
            "default": "100, 200, 250, 50"
        },
        {
            "name": "predicted",
            "label": "Forecast values, same order",
            "type": "textarea",
            "default": "110, 180, 250, 60"
        }
    ],
    "outputs": [
        {
            "key": "mape",
            "label": "MAPE",
            "format": "percent"
        },
        {
            "key": "smape",
            "label": "sMAPE (symmetric, 0–200%)",
            "format": "percent"
        },
        {
            "key": "wape",
            "label": "WAPE (Σ|error| ÷ Σ|actual|)",
            "format": "percent"
        },
        {
            "key": "accuracy",
            "label": "Forecast accuracy (100% − MAPE, floored at 0)",
            "format": "percent"
        },
        {
            "key": "n_used",
            "label": "Pairs used for MAPE",
            "format": "integer"
        },
        {
            "key": "excluded",
            "label": "Pairs excluded (actual = 0)",
            "format": "integer"
        }
    ],
    "formula": {
        "text": "MAPE = 100% ÷ n × Σ|Aᵢ − Fᵢ| ÷ |Aᵢ| (Aᵢ ≠ 0);  sMAPE = 100% ÷ n × Σ 2|Aᵢ − Fᵢ| ÷ (|Aᵢ| + |Fᵢ|);  WAPE = 100% × Σ|Aᵢ − Fᵢ| ÷ Σ|Aᵢ|",
        "variables": {
            "Aᵢ": "actual value",
            "Fᵢ": "forecast value",
            "n": "pairs included in each measure"
        }
    },
    "worked_example": {
        "inputs": {
            "actual": "100, 200, 250, 50",
            "predicted": "110, 180, 250, 60"
        },
        "steps": [
            "Percentage errors: 10/100 = 10%, 20/200 = 10%, 0%, 10/50 = 20%.",
            "MAPE = (10 + 10 + 0 + 20) ÷ 4 = 10%.",
            "WAPE = (10 + 20 + 0 + 10) ÷ 600 = 6.67%, lower because the large-volume items were forecast well."
        ],
        "result": "The forecasts have a MAPE of 10% and a volume-weighted error (WAPE) of 6.7%."
    },
    "interpretation": "MAPE treats a 10-unit miss on a small item as worse than the same miss on a big one. That suits comparing items, but small actual values can blow it up, and it punishes over-forecasts more than under-forecasts. WAPE weights by volume and is usually the better single number for totals such as inventory.",
    "assumptions": [
        "Errors are measured on actual values: MAPE skips pairs where the actual is 0 and reports how many were skipped.",
        "sMAPE uses the 0–200% definition with (|A| + |F|) ÷ 2 in the denominator, skipping pairs where both are 0."
    ],
    "limitations": [
        "MAPE is unstable when actual values are close to zero, and meaningless for data that can be negative or have an arbitrary zero (such as temperatures).",
        "“Accuracy = 100% − MAPE” is a reporting convention, not a statistical measure."
    ],
    "rules": null,
    "faqs": [
        {
            "question": "What is a good MAPE?",
            "answer": "It depends on how predictable the series is. Compare against a simple benchmark, such as last period’s value, rather than a fixed threshold."
        },
        {
            "question": "Why do MAPE and WAPE differ?",
            "answer": "MAPE averages percentages, so every item counts equally. WAPE divides total error by total volume, so big items count more."
        }
    ],
    "sources": [],
    "related": [
        {
            "name": "Mean Absolute Error Calculator",
            "url": "https://calculators.geekmidst.com/calculators/mean-absolute-error-calculator"
        },
        {
            "name": "RMSE Calculator",
            "url": "https://calculators.geekmidst.com/calculators/root-mean-square-error-calculator"
        },
        {
            "name": "Percent Error Calculator",
            "url": "https://calculators.geekmidst.com/calculators/percent-error-calculator"
        }
    ],
    "disclaimer": null,
    "publisher": "GM¹X CalcVerse"
}