I am trying to select a regression model for datasets with a right-skewed outcome and where "outliers" are present (where outliers are very high values due to the nature of the data). The data are time-sensitive so for model building I am splitting the dataset in train and test sets based on time (no k-fold CV) and the aim is to get the best model in terms of predictive accuracy.
Given the skewness of the outcome and the presence of outliers, it seems that RMSE and R^2 are not very suitable. What would be the best error metric to use to evaluate different models (e.g. features and hyperparameters) in terms of predictive accuracy? The size of the dataset is small (in the range of 500 for training and 100 for testing) so even a tiny number of outliers can alter my evaluation results.