Objective metrics
Autopilot produces accuracy metrics to evaluate the model candidates and help you choose which to use to generate forecasts. You can either let Autopilot optimize the predictor for you, or you can manually choose an algorithm for your predictor. By default, Autopilot uses the Average Weighted Quantile Loss.
The following list contains the names of the metrics that are currently available to measure the performance of models for timeseries forecasting.
RMSE

Root mean squared error (RMSE) – Measures the square root of the squared difference between predicted and actual values, and is averaged over all values. It's an important metric to indicate the presence of large model errors and outliers. Values range from zero (0) to infinity, with smaller numbers indicating a better model fit to the data. RMSE is dependent on scale, and should not be used to compare datasets of different sizes.
wQL

Weighted Quantile Loss (wQL) – Assess the accuracy of the forecast by measuring the weighted absolute differences between predicted and actual P10, P50, and P90 quantiles with lower values indicating better performance.
Average wQL (default)

Average Weighted Quantile Loss (Average wQL) – Evaluates the forecast by averaging the accuracy at the P10, P50, and P90 quantiles. A lower value indicates a more accurate model.
MASE

Mean Absolute Scaled Error (MASE) – The mean absolute error of the forecast normalized by the mean absolute error of a simple baseline forecasting method. A lower value indicates a more accurate model, where MASE < 1 is estimated to be better than the baseline and MASE > 1 is estimated to be worse than the baseline.
MAPE

Mean Absolute Percent Error (MAPE) – The percentage error (percent difference of the mean forecasted value versus the actual value) averaged over all time points. A lower value indicates a more accurate model, where MAPE = 0 is a model with no errors.
WAPE

Weighted Absolute Percent Error (WAPE) – The sum of the absolute error normalized by the sum of the absolute target, which measure the overall deviation of forecasted values from observed values. A lower value indicates a more accurate model.