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标题：USING THE R-MAPE INDEX AS A RESISTANT MEASURE OF FORECAST ACCURACY
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作者：Juan José Montaño Moreno ; Alfonso Palmer Pol ; Albert Sesé Abad 等
期刊名称：Psicothema
印刷版ISSN：0214-9915
电子版ISSN：1886-144X
出版年度：2013
卷号：25
期号：4
页码：500-506
DOI：10.7334/psicothema2013.23
出版社：Cologio Oficial de Psicólogos del Principado
摘要：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. Method: 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.
关键词：Time series; error measures; outliers; neural networks; ARIMA models.