How can you check the accuracy of a revenue prediction forecast in Oracle Analytics?

Highlighting the prediction interval is crucial for assessing the accuracy of a revenue prediction forecast in Oracle Analytics. This interval provides a range that represents where the actual revenues are likely to fall based on the forecast model. By visualizing the prediction interval, users can understand the degree of uncertainty associated with the forecast and see how much variability there is around the predicted value.

The prediction interval encompasses potential fluctuations in revenue that can result from various factors, including market conditions, seasonal trends, and economic shifts. By examining the interval, analysts can gauge the reliability of their forecasts and make informed decisions. If the actual revenue data falls within the prediction interval, it suggests that the model has provided a reasonably accurate forecast.

In contrast, while adding reference lines for maximum and minimum values can provide context for the data, it does not directly address the uncertainty of the forecast. Changing the model used by the algorithm might yield different predictions but doesn’t inherently validate the accuracy of the existing forecast. Combining a polynomial trend line with a reference line enhances data visualization but does not evaluate or check the accuracy of the forecast itself. Therefore, highlighting the prediction interval is the most effective way to assess forecast accuracy in Oracle Analytics.