Applicability of the Revised Mean Absolute Percentage Errors (MAPE) Approach to Some Popular Normal and Non-normal Independent Time Series

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 Average Methods when coefficients of variation (c.v.) are smaller than 0.4 or is much greater than 4.0 for those independent time series. For independent time series with moderate c.v.'s, the complexity from the ratios of MAPE and RMAPE will mislead researchers on distinguishing the forecasting accuracies from different Moving Average Methods. The complexity from the ratios will be released only when the c.v. is very small, or when the c.v. is very large. Therefore, when data are from independent time series, the Mean Absolute Deviation (MAD) reveals valid the forecasting accuracies from various Moving Average Methods, but not from MAPE or RMAPE.