Evaluate the Model Based on MASE (Mean Absolute Scaled Error)

This function computes MASE given prediction result versus evaluation set.

Usage

mase(x, y, ...)

# S3 method for class 'predbvhar'
mase(x, y, ...)

# S3 method for class 'bvharcv'
mase(x, y, ...)

Arguments

x

Forecasting object

y

Test data to be compared. should be the same format with the train data.

...

not used

Value

MASE vector corresponding to each variable.

Details

Let \(e_t = y_t - \hat{y}_t\). Scaled error is defined by $$q_t = \frac{e_t}{\sum_{i = 2}^{n} \lvert Y_i - Y_{i - 1} \rvert / (n - 1)}$$ so that the error can be free of the data scale. Then

$$MASE = mean(\lvert q_t \rvert)$$

Here, \(Y_i\) are the points in the sample, i.e. errors are scaled by the in-sample mean absolute error (\(mean(\lvert e_t \rvert)\)) from the naive random walk forecasting.

References

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.