How is accuracy determined using the statistical error function?

Accuracy is primarily determined using various statistical measures because this approach enables a comprehensive assessment of how well the forecasts align with actual outcomes. Statistical measures provide quantitative insights that can capture different facets of performance, such as bias, variability, and overall error magnitude. This enables practitioners to evaluate the effectiveness of their forecasting methods across a range of scenarios and timeframes.

In contrast, relying solely on a single historical actual value would not provide a complete picture of performance, as it doesn't account for fluctuations or trends over time. Customer feedback, although valuable, is subjective and may introduce biases that do not reflect statistical accuracy. Similarly, comparing forecast values to industry standards can offer some insights, but it does not directly contribute to calculating accuracy within a specific model or dataset, which often necessitates more granular statistical assessment to support effective forecasting practices.