mase {bvhar}

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

Description

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

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.

Value

MASE vector corresponding to each variable.

References

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