One embodiment provides optimizing predictive precision for actionable forecasts of revenue change including receiving revenue data with timestamps for a number of historical periods at a particular level, with attributes of the particular level and a percentage of the required revenue change. The data is filtered. The filtered data is aggregated at the particular level for a selected prediction. A sliding window of the number of historical periods is moved over business periods, creating a data point for each historical period temporal window by extracting features. A required target output is created for each data point for at least one future time period. A model is trained to optimize predictive precision for actionable forecasts of revenue change. A set of recent histories is converted into a quantitative health value.