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INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences, 2012
In order to establish a high-precision nonlinear forecasting model, the paper presents a new global optimization technique for parameters optimization in nonlinear forecasting model based on the minimization of mean absolute percentage error (MAPE). By implementation of an optimization technique based on the successive use of a genetic algorithm and of
Haijun Chen - +3 more
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In order to establish a high-precision nonlinear forecasting model, the paper presents a new global optimization technique for parameters optimization in nonlinear forecasting model based on the minimization of mean absolute percentage error (MAPE). By implementation of an optimization technique based on the successive use of a genetic algorithm and of
Haijun Chen - +3 more
openaire +1 more source
Modelling DGM(1,1) under the Criterion of the Minimization of Mean Absolute Percentage Error
2009 Second International Symposium on Knowledge Acquisition and Modeling, 2009In this paper, we present linear programming method in order to estimate the parameters of the DGM(1,1) model under the criterion of the minimization of mean absolute percentage error (MAPE) (some authors called average relative error). A published article is chosen for practical tests of this method, the results show that this method can obviously ...
Lifeng Wu, Yinao Wang
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Mean absolute percentage error and bias in economic forecasting
Economics Letters, 2011Abstract This article develops a simple theoretical framework to show how forecasters may bias downward point predictions under the assumption that the asymmetric loss function is directly related to the (Mean) Absolute Percentage Error (M)APE.
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International Advances in Economic Research, 2009
Commonly used Mean Absolute Percentage Errors (MAPE) and the authors’ revised Mean Absolute Percentage Errors (RMAPE) are applied to measure the forecasting accuracy from different Moving Average Methods for independent time series. Simulation results show that both MAPE and RMAPE can only provide sensitive forecasting accuracy measurements on Moving ...
Louie Ren, Yong Glasure
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Commonly used Mean Absolute Percentage Errors (MAPE) and the authors’ revised Mean Absolute Percentage Errors (RMAPE) are applied to measure the forecasting accuracy from different Moving Average Methods for independent time series. Simulation results show that both MAPE and RMAPE can only provide sensitive forecasting accuracy measurements on Moving ...
Louie Ren, Yong Glasure
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Measuring Relative Accuracy: A Better Alternative to Mean Absolute Percentage Error
SSRN Electronic Journal, 2013Surveys show that the mean absolute percentage error (MAPE) is the most widely used measure of forecast accuracy in businesses and organizations. It is also used to compare accuracy across multiple data sets, e.g. when choosing a forecasting method. Yet this metric systematically favours methods which under-forecast.
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Mean Squared Error, Deconstructed
Journal of Advances in Modeling Earth Systems, 2021Timothy O Hodson +2 more
exaly
Root mean square error or mean absolute error? Use their ratio as well
Information Sciences, 2022exaly
On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression
IEEE Signal Processing Letters, 2020Jun Qi +2 more
exaly