Errors that are not distributed symmetrically about the mean. This is common when trends are expressed in units (not percentages) and when there are large changes in the variable of interest. The forecaster might formulate the model with original data for a variety of reasons such as the presence of large measurement errors. As a result, forecast errors would tend to be skewed, such that they would be larger for cases when the actual (for the dependent variable) exceeded the forecasts. To deal with this, transform the forecasted and actual values to logs and use the resulting errors to construct prediction intervals (which are more likely to be symmetric), and then report the prediction intervals in original units (which will be asymmetric). However, this will not solve the asymmetry problem for contrary series. For details, see Armstrong and Collopy (2001).