Identify and Adjust Forecast Bias
Discover advanced techniques for improving forecast accuracy by identifying biases and refining predictions
Discover advanced techniques for improving forecast accuracy by analyzing patterns, identifying biases, and refining predictions. Businesses can then refine their models to avoid recurring inaccurate biases.
- Graphing Forecasts vs. Actuals: Use scatterplots to identify patterns of bias, such as overoptimism or sandbagging, by comparing forecasted amounts to actual results.
- Overoptimism and Sandbagging: Graphs can reveal consistent overestimation (overoptimism) or underestimation (sandbagging) in forecasts, helping to adjust future predictions.
- Variance Statistical Calculations: Metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Weighted Average Percentage Error (WAPE) quantify forecast accuracy and expose offsetting errors.
- Adjusting Forecasts for Bias: Use past variance statistics to adjust future forecasts, either by applying weighted adjustments (WAPE) or regression-based corrections for slope and intercept.