Robust Measures for Forecast Accuracy I am doing a forecast using robust exponential smoothing methods and to determine / measure the forecast accuracy I want to use robust measurements as well. As I am not really familiar with robust measures I would like you to ask what good robust accuracy measures exist? 
I have found MADM/median here on stackexchange (see below the quote) which seems to be appropriate in my case. For the calculation of the MADM I would use for xi the error difference between real data and predicted. x would be the vector of all errors. However, I could not detect any academic discourse on the proposed MADM/median.

You may want to look into the median absolute deviation from the median (MADM). That is: MADM=median(|xi−median(x)|) I suspect a
  better nonparametric analogy to the coefficient of variation would be
  MADM/median, rather than IQR/median.
(cf. here the answer of gung at Oct 5 '12 at 14:41:
  A robust (non-parametric) measure like Coefficient of Variation -- IQR/median, or alternative?)

 A: Firstly, it is important to decide what is the loss function you want to use in your evaluation.
In their recent review of alternative error measures, (Davydenko and Fildes, 2016) recommend the use of the symmetric linear loss because of the following reasons:

Fitting a statistical model usually delivers forecasts optimal under quadratic loss. This, e.g., happens when we fit a linear regression. If our density forecast from statistical modelling is symmetric, then forecasts optimal under quadratic loss are also optimal under linear loss. But, if we stabilise the variance by log-transformations and then transform back forecasts by exponentiation, we get forecasts optimal only under linear loss. If we use another loss, we must first obtain the density forecast using a statistical model, and then adjust our estimate given our specific loss function (see examples of doing this in Goodwin, 2000).
Let’s assume we want to empirically compare two methods and find out which method is better in terms of a symmetric linear loss (since this type of loss is commonly used in modelling). If we have only one time series, it seems natural to use a mean absolute error (MAE). Also, MAE is attractive as it is simple to understand and calculate (Hyndman, 2006).

Then they notice that

Potentially, MAE has the following limitation: absolute errors follow a highly skewed distribution with aheavy right tail, which means that MAE is not robust (in other words, it is a highly inefficient estimate).

(Davydenko and Fildes, 2016) propose the following solution to define a robust MAE measure:

If distribution of absolute errors is heavily skewed, MAE becomes a very inefficient estimate of the expected value of absolute error. One simple method to improve the efficiency of the estimates while not introducing substantial bias is to use asymmetric trimming algorithms, such as those described by (Alkhazeleh and Razali, 2010).

References:
Davydenko, A., & Fildes, R. (2016).Forecast Error Measures: Critical Review and Practical Recommendations. In Business Forecasting: Practical Problems and Solutions.John Wiley & Sons Inc.
The full text is available here.
