Error Analysis

You can use the Error Analysis method to determine the algorithm for error calculation. The Error Analysis method is calculated for all selected Forecast Methods. The winner is the forecast method with the lowest error.

  1. From the menu, select Data > Statistical Forecast Methods > Competitive Forecast Methods.
  2. Select a parameter and then click New or Edit.
  3. In Competitive Forecast Properties, select Error Analysis.
  4. In Method, select the applicable forecast method.

    This table shows the available forecast methods:

    Formula Description
    Symmetric MAPE (SMAPE) Symmetric MAPE (SMAPE) measures the percentage difference between the forecast and actual demand, where both values contribute equally to the denominator:
    SMAPE = |Actual − Forecast| / ((|Actual| + |Forecast|) / 2)

    A value of 0 means a perfect forecast. A value of 0.05 means the forecast deviates approximately 5% from actual demand.

    Unlike traditional MAPE, which divides only by the actual value, SMAPE treats over-forecasting and under-forecasting equally. This avoids bias where forecasting 100 when the actual value is 50 (100% error) is penalized differently than forecasting 50 when the actual value is 100 (50% error). With SMAPE, both cases produce the same error.

    This makes SMAPE particularly suitable for competitive forecast selection, where methods are compared on a fair and balanced basis regardless of whether they tend to forecast above or below actual demand.
  5. In Limits, specify values in the Lower and Upper limits. If the calculation of the error analysis method is outside these limits, the forecast method is not selected as the winner. You can perform competitive forecasting without finding a winner that meets the criteria. You can define a scoreboard to identify key records without a winner for manual inspection. You can determine a different forecast method or competitive method that is better suited.
  6. In Period Range, specify this information:
    Use Forecast defined period range
    Select this option to limit or control the periods to which the forecast applies.
    Hold-out periods (before forecast point)
    Specify a value to define the periods used for the Error Analysis. Hold-out periods are three monthly periods and a maximum of one-third of the total available periods.
    See Hold-out periods.
    Use Smart Combined Forecast
    Select this option to combine multiple forecasting models to generate a more accurate prediction.
    See Smart Combined Forecast.