Final answer:
To calculate the MAD and MSE, absolute and squared deviations between forecasted and actual values are needed, summed up, and divided by the number of observations. These measures evaluate the accuracy of the forecasts, with the MAD offering a linear average and MSE emphasizing larger errors.
Step-by-step explanation:
To calculate the mean absolute deviation (MAD) and the mean squared error (MSE) of the forecasts, you would first need to have the actual values and the forecasted values. Once you have these values, you would follow these steps:
- Calculate the absolute deviations by subtracting each forecasted value from the corresponding actual value and taking the absolute value of the result.
- Sum up all the absolute deviations and divide by the number of observations to get the MAD.
- Calculate the squared deviations by squaring the difference between each forecasted value and the corresponding actual value.
- Sum up all the squared deviations and divide by the number of observations to get the MSE.
Without the actual data points, it is not possible to provide numeric answers for the MAD and MSE. It's important to compare these measures to assess the accuracy of the forecasts. The MAD provides a linear measure of average error magnitude, while the MSE gives more weight to larger errors due to squaring the deviations.