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.

Question

Quizplus · Accepted Answer

Mean absolute error, mean squared error, and mean absolute percentage error all measure how well a forecasting method is able to reproduce the historical data that is already available. In other words, they measure the accuracy of the model's predictions compared to the actual values in the time series dataset.

The Mean Absolute Error, Mean Squared Error, and Mean Absolute