Final answer:
As a manager, the preferred forecasting method would be the one with lower MAE, MAPE, MSE, and TS values, indicating higher accuracy and less variance in forecasting. These metrics help determine the method's relative and absolute forecasting accuracy compared to actual values and benchmarks.
Step-by-step explanation:
As a manager comparing two forecasting methods using error metrics such as MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), MSE (Mean Squared Error), and TS (Theil's U Statistic), you would want to choose the method that presents the lowest values in these metrics, indicating higher accuracy and reliability of the forecasts.
Mean Absolute Error (MAE) provides a straightforward measure of the magnitude of errors in forecasting without considering their direction. A lower MAE suggests that the forecast is more accurate on average.
Mean Absolute Percentage Error (MAPE) offers an understanding of error as a percentage of the actual values, which is useful for comparability across different scales. A lower MAPE indicates better predictive accuracy in relative terms.
Mean Squared Error (MSE) is a common measure that squares the errors before averaging, thus penalizing larger errors more than MAE. The method with a lower MSE would typically be favored as it suggests less variance in forecasting errors.
Theil's U Statistic (TS) compares the forecasted series against a naive or simple benchmark forecast. Values closer to zero mean that the forecasting method is better than the naive benchmark.
In conclusion, as a manager, you would likely prefer the forecasting method that shows lower MAE, MAPE, MSE, and TS values, but keep in mind the context and specific considerations for your business when making the final decision.