A data scientist evaluates a GluonTS DeepAR model and finds coverage scores of 0.489 at the 0.5 quantile and 0.889 at the 0.9 quantile on the test set. What is a reasonable conclusion about the distributional forecast calibration?

Coverage scores in probabilistic forecasting measure how often the true value falls within a predicted quantile interval. For a well-calibrated model, the coverage score should be approximately equal to the corresponding quantile level. In this case, a 0.5 quantile should have a coverage score near 0.5, and a 0.9 quantile should have a coverage score near 0.9. The given scores of 0.489 and 0.889 are very close to their respective quantile levels, indicating good calibration. Incorrect options suggest misinterpretations of coverage scores; they are not expected to be equal across quantiles, peak at the median, or always be below the quantile value.