What happens to negative values during the statistical forecasting process?

During the statistical forecasting process, negative values are adjusted automatically or flagged for review. This is crucial because negative values can indicate issues such as returns or cancellations, which need to be addressed to ensure accurate forecasting.

Automatically adjusting these values helps maintain the integrity of the forecasting model by preventing skewed results that could arise from counting negative values as valid data points. Flagging allows users to review these cases individually, ensuring that any potential errors or necessary adjustments are accounted for. This process ensures that the forecasting remains reliable and reflective of real-world conditions.

In contrast, ignoring negative values entirely would lead to incompleteness in data analysis, while manual processing by a finance team can introduce delays and increase the potential for human error. Converting negative values to positive does not capture the true nature of the data, which could be misleading in a forecasting context.