Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Qiyaasta Saxnaanta Saadaasha: MAPE, sMAPE iyo Kediska MASE

Is barbar dhig boqolleyda iyo khaladaadka saadaasha la miisaamay, xakamee xaqiiqooyinka eber oo turjumaan qiyamka mitirka marka loo eego bartilmaameedka naive.

Wadooyinka haga ee la xidhiidhaSaadaasha Saxnaanta Metrics Mape iyo Smape
Su'aal 1 ee 8

Actual demand is 100 and the forecast is 90. What is the absolute percentage error under MAPE?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Actual demand is 100 and the forecast is 90. What is the absolute percentage error under MAPE?
    • 10%
    • 90%
    • 100%
    • 0.1%
  2. What issue arises for MAPE when an actual value is zero?
    • The forecast must also be zero
    • Its percentage denominator is zero
    • The metric becomes a probability
    • The error is automatically counted as perfect
  3. What does MASE below one indicate under its usual definition?
    • The model's error is below one target unit
    • Every test forecast is more accurate than the baseline
    • Test MAE is below the selected in-sample naive MAE scale
    • The model has zero bias
  4. Why state the exact sMAPE formula in a report?
    • It removes forecast bias automatically
    • sMAPE has only one universal zero definition
    • The metric is always expressed in target units
    • Implementations use different scaling and zero conventions
  5. Why can MAPE overemphasize low-volume observations?
    • It divides each absolute error by the training-set naive error scale
    • A fixed absolute miss becomes a larger percentage when the actual is small
    • It gives greater percentage weight to high-volume observations
    • It averages signed percentage errors so positive and negative misses cancel
  6. For seasonal data, which MASE denominator may be more relevant?
    • The test-set standard deviation only
    • The largest observed forecast error
    • A seasonal-naive training error at the matching period
    • The training mean squared error
  7. If the training naive MAE denominator is zero, what happens to MASE?
    • It becomes the number of forecasts
    • Had iyo jeer waa eber
    • It equals MAPE
    • It is undefined without a defined alternative convention
  8. Why evaluate forecasts using the same time windows and origins?
    • To make model comparisons use equivalent information and cases
    • To make all actual values nonzero
    • To guarantee stationarity
    • To eliminate the need for a baseline