Forecasting Accuracy Metrics

Forecasting accuracy metrics evaluate how closely your predictions match actual outcomes. Common metrics include Mean Absolute Error (MAE), which measures the average size of errors regardless of direction; Mean Squared Error (MSE), penalizing larger errors more heavily; and Mean Absolute Percentage Error (MAPE), expressing errors as a percentage for comparison across different scales. These help identify the reliability of forecasts, guiding improvements. A lower error indicates more accurate predictions, enhancing decision-making and planning processes.