MAPE

Measure of prediction accuracy of a forecast

The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics. It usually expresses the accuracy as a ratio defined by the formula: MAPE = 100 1 n ∑ t = 1 n | A t − F t A t | {\displaystyle {\mbox{MAPE}}=100{\frac {1}{n}}\sum _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|} Where At is the actual value and Ft is the forecast value.

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MAPE

Measure of prediction accuracy of a forecast

Texte en anglais

The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics. It usually expresses the accuracy as a ratio defined by the formula: MAPE = 100 1 n ∑ t = 1 n | A t − F t A t | {\displaystyle {\mbox{MAPE}}=100{\frac {1}{n}}\sum _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|} Where At is the actual value and Ft is the forecast value.

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Texte en anglais Pas encore d'article dans ta langue : extrait en anglais.

The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics. It usually expresses the accuracy as a ratio defined by the formula: MAPE = 100 1 n ∑ t = 1 n | A t − F t A t | {\displaystyle {\mbox{MAPE}}=100{\frac {1}{n}}\sum _{t=1}^{n}\left|{\frac {A_{t}-F_{t}}{A_{t}}}\right|} Where At is the actual value and Ft is the forecast value. Their difference is divided by the actual value At. The absolute value of this ratio is summed for every forecasted point in time and divided by the number of fitted points n. MAPE should be used with extreme caution in forecasting, because small actuals (target labels) can lead to highly inflated MAPE scores. wMAPE should be used instead of MAPE wherever possible (see section below).

Texte : Wikipédia en anglais, CC BY-SA 4.0. · Image : Octopus team Centrale Paris 2018 (CC BY-SA 4.0) ·

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