title: Using the R-MAPE index as a resistant measure of forecast accuracy.
abstract: BACKGROUND
  The mean absolute percentage error (MAPE) is probably the most widely used goodness-of-fit measure. However, it does not meet the validity criterion due to the fact that the distribution of the absolute percentage errors is usually skewed to the right, with the presence of outlier values. In these cases, MAPE overstates the corresponding population parameter. In this study, we propose an alternative index, called Resistant MAPE or R-MAPE based on the calculation of the Huber M-estimator, which allows overcoming the aforementioned limitation.
  
  
  METHODS
  The results derived from the application of Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models are used to forecast a time series.
  
  
  RESULTS
  The arithmetic mean, MAPE, overstates the corresponding population parameter, unlike R-MAPE, on a set of error distributions with a statistically significant right skew, as well as outlier values.
  
  
  CONCLUSIONS
  Our results suggest that R-MAPE represents a suitable alternative measure of forecast accuracy, due to the fact that it provides a valid assessment of forecast accuracy compared to MAPE.
authors.affiliation: Universidad de las Islas Baleares.
authors.forename: Juan José
authors.initials: JJ
authors.lastname: Montaño Moreno
authors.forename: Alfonso
authors.initials: A
authors.lastname: Palmer Pol
authors.forename: Albert
authors.initials: A
authors.lastname: Sesé Abad
authors.forename: Berta
authors.initials: B
authors.lastname: Cajal Blasco
mesh_terms: D000465:Algorithms; D005544:Forecasting; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results