Which evaluation metrics are applicable for assessing the quality of a time-series forecasting model? (Choose two.)

Root Mean Square Error (RMSE) is a standard metric for regression tasks, including time-series forecasting. It measures the average magnitude of the errors, giving higher weight to larger errors, which is crucial for understanding prediction accuracy. Average weighted quantile loss (wQL) is particularly useful for probabilistic forecasting, where the model predicts a range of possible outcomes (quantiles) rather than a single point estimate. It assesses the quality of these quantile predictions, which is often desired in time-series to understand uncertainty. Recall and LogLoss are metrics primarily used for classification problems. InferenceLatency measures the time taken for a model to make a prediction, not its predictive quality.