What do the bucket actuals, fit, predict, and predicted actuals functions all require in common?

The functions of bucket actuals, fit, predict, and predicted actuals focus on analyzing and processing historical data and forecast parameters to generate insights and projections. These functions rely on a common input worksheet that describes forecast parameters, as this information is essential for determining patterns, trends, and making accurate predictions.

Forecast parameters include variables and settings that influence the modeling of forecasts, such as seasonality, trends, and error tolerances. By utilizing consistent parameters across these functions, analysts can ensure that the calculations and outcomes are aligned, which facilitates more accurate and cohesive data analysis.

The other options are less relevant because while historical averages, customer demographics, and product specifications might play a role in overall data analysis, they do not directly pertain to the core requirements for executing bucket actuals, fit, predict, and predicted actuals functions. The focus here is specifically on the need for consistent and coherent forecast parameters, which are crucial for successful output across these analytical functions.