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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 .
Percentage error; Mean absolute percentage error; Mean squared error; Mean squared prediction error; Minimum mean-square error; Squared deviations; Peak signal-to-noise ratio; Root mean square deviation; Errors and residuals in statistics; References. Khan, Aman U.; Hildreth, W. Bartley (2003). Case studies in public budgeting and financial ...
Relative change. In any quantitative science, the terms relative change and relative difference are used to compare two quantities while taking into account the "sizes" of the things being compared, i.e. dividing by a standard or reference or starting value. [1] The comparison is expressed as a ratio and is a unitless number.
In contrast to the mean absolute percentage error, SMAPE has both a lower bound and an upper bound. Indeed, the formula above provides a result between 0% and 200%. Indeed, the formula above provides a result between 0% and 200%.
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) ...
Definition and basic properties. The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
Forecast errors can be evaluated using a variety of methods namely mean percentage error, root mean squared error, mean absolute percentage error, ...
The average of all the sample absolute deviations about the mean of size 3 that can be drawn from the population is 44/81, while the average of all the sample absolute deviations about the median is 4/9. Therefore, the absolute deviation is a biased estimator. However, this argument is based on the notion of mean-unbiasedness.