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Confusingly, sometimes when people refer to wMAPE they are talking about a different model in which the numerator and denominator of the wMAPE formula above are weighted again by another set of custom weights .
Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero. See also
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(200% for the first formula and 100% for the second formula). Provided the data are strictly positive, a better measure of relative accuracy can be obtained based on the log of the accuracy ratio: log( F t / A t ) This measure is easier to analyse statistically, and has valuable symmetry and unbiasedness properties.
For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters.
Mean absolute percentage error; Mean percentage error; Symmetric mean absolute percentage error; References This page was last edited on 2 April 2024, at 22:32 (UTC) ...
Forecast errors can be evaluated using a variety of methods namely mean percentage error, root mean squared error, mean absolute percentage error, ...
For a value that is sampled with an unbiased normally distributed error, ... formula for standard error: ... by sampling close to a larger percentage of the ...