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. adjusting the scale of the data.

b. determining how well a particular forecasting method is able to reproduce the time series data that are already available.

c. predicting the future values and wait for a pre-defined time period to examine how accurate the predictions were.

d. using the current value to estimate how well the model generates previous values correctly.

Correct Option:

These methods measure forecast accuracy by determining how well a particular forecasting method is able to reproduce the time series data that are already available.

Option: B

Explanation:

The forecast reliability is the level of proximity of the quantity assertion to the real or true value of that quantity in statistics. Typically, at the time the prediction is produced, the real value can not be calculated, as the assumption involves the future.

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ogorwyne ogorwyne · Answer 2 · 2021-11-25T13:05:34+01:00

These methods measures forecast accuracy by determining how well a particular forecasting method is able to reproduce the time series data that are already available.

These three are referred to as the forecasts of accuracy. They help to measure the number of errors that are in a former forecast.

Then they would factor these errors into a new forecast while making adjustments in this new forecast.