What should be done to check the accuracy of a forecast in a visualization?

To determine the accuracy of a forecast in a visualization, adding reference lines for maximum and minimum values is an effective approach. These reference lines provide a visual benchmark against which the forecast can be compared, allowing you to see how the predicted values relate to the observed data. By establishing boundaries for what is considered a normal range of variability, you can evaluate whether the forecast falls within an expected range.

Reference lines help in identifying outliers or patterns in the data that may suggest the forecast is not accurate. They serve as critical indicators for assessing the quality of the forecast, as any significant deviations beyond these lines may warrant further investigation into the model's reliability and validity.

In contrast, changing the model used by the algorithm or combining forecasts with polynomial trend lines may improve overall forecast quality but do not provide a direct method for checking accuracy within the visualization itself. Highlighting the prediction interval is another method, though it focuses more on the uncertainty of the predictions rather than directly assessing accuracy against actual observed data.