Closed dwolffram closed 1 year ago
I'm wondering why there is a strict inequality in the definition of the (quantile) coverage (https://github.com/awslabs/gluonts/blob/dev/src/gluonts/evaluation/metrics.py#L84):
np.mean(target < forecast)
Shouldn't this be np.mean(target <= forecast)? Right now, if you predict 0 and observe 0, it does not count as covered. Or am I missing something?
np.mean(target <= forecast)
Thanks!
Related: the same definition is used in gluonts.ev, see https://github.com/awslabs/gluonts/blob/dev/src/gluonts/ev/stats.py#L50
gluonts.ev
Description
I'm wondering why there is a strict inequality in the definition of the (quantile) coverage (https://github.com/awslabs/gluonts/blob/dev/src/gluonts/evaluation/metrics.py#L84):
np.mean(target < forecast)
Shouldn't this be
np.mean(target <= forecast)
? Right now, if you predict 0 and observe 0, it does not count as covered. Or am I missing something?Thanks!