As with all estimates I care about the error and not much else. The only error measure provided is mean absolute error which I find a little bit unsatisfying. It gives only very little information about the behavior of the error.
The easiest is to compute the error for every sample and then show a histogram of it.
Why you might wonder? In this case we deal with varying -though often over time stabalizing- data. This means that sometimes the error is actually greater and then very little. If you take the absoluate value of it and take the average (MAE), you might underestimate (resp overestimate) the error at times. In this case I might expect a tri-modal error distribution if we consider positive & negative errors.
A histogram is much more expressive (and oh so simple to generate) than a simple MEAN, STD DEV, MAE or MSE.