AutoML can only use the mean squared error (MSE) metric to evaluate the performance of a time series forecasting model.

AutoML can use a variety of evaluation metrics to assess the performance of a time series forecasting model, such as mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared.

AutoML can only use the mean absolute percentage error (MAPE) metric to evaluate the performance of a time series forecasting model.

AutoML cannot use any evaluation metrics to assess the performance of a time series forecasting model, as it relies solely on the model's predictions to evaluate performance.