The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast.These methods measure forecast accuracy by
a. determining how well a particular forecasting method is able to reproduce the time series data that are already available.
b. using the current value to estimate how well the model generates previous values correctly.
c. predicting the future values and wait for a pre-defined time period to examine how accurate the predictions were.
d. adjusting the scale of the data.
a
RATIONALE: These measures of forecast accuracy are used to determine how well a particular forecasting method is able to reproduce the time series data that are already available. By selecting the method that is most accurate for the data already known, we hope to increase the likelihood that we will obtain more accurate forecasts for future time periods.
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