i was wondering what is the differences between Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) in determining the accuracy of a forecast? Which one is better? Thanks
MSE is scale-dependent, MAPE is not. So if you are comparing accuracy across time series with different scales, you can't use MSE.
For business use, MAPE is often preferred because apparently managers understand percentages better than squared errors.
MAPE can't be used when percentages make no sense. For example, the Fahrenheit and Celsius temperature scales have relatively arbitrary zero points, and it makes no sense to talk about percentages. MAPE also cannot be used when the time series can take zero values.
MASE is intended to be both independent of scale and usable on all scales.
As @Dmitrij said, the
accuracy() function in the
forecast package for R is an easy way to compute these and other accuracy measures.
There is a lot more about forecast accuracy measures in my 2006 IJF paper with Anne Koehler.