What output is expected when comparing historical actual values with forecast values?

When comparing historical actual values with forecast values, the expected output is measures of accuracy and errors. This process involves assessing how well the forecast aligned with the actual performance over time, which provides critical insights into the effectiveness of the forecasting model.

Such measures may include various statistical metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), or Root Mean Square Error (RMSE), which quantify the discrepancies between predicted and actual values. Understanding these errors helps organizations to evaluate the reliability of their forecasts and make informed decisions about adjustments or improvements to their forecasting techniques.

As a result, these accuracy measures are essential for continuous improvement in forecasting processes, allowing data-driven adjustments and enhancing future forecasting efforts. Ultimately, this analysis directly informs the overall performance evaluation of the organization's predictive capabilities and helps guide subsequent forecasting adjustments and strategies.