Casual Dynamic Model for Revenue

A non-transitory computer-readable data storage medium to store a computer program, execution of the computer program by a processor causing a method to be performed, the method comprising:

• performing real-time forecasting of revenue using a causal dynamic model for the revenue based on one or more particular drivers that affect or relate to revenue,
• wherein the causal dynamic model is constructed by:
• identifying a plurality of drivers that affect or relate to revenue to be forecast, each driver being a variable, each particular driver being one of the drivers identified;
• for each driver, performing cross-correlation to identify lag between the revenue and the driver;
• for each driver of one or more of the drivers, synchronizing the revenue and the driver, based on the lag between the revenue and the driver;
• selecting the particular drivers from the one or more of the drivers, based on an analysis of the lags between the revenue and the one or more of the drivers as synchronized;
• and,
• constructing the causal dynamic model for the revenue, using the particular drivers selected.

A system comprising:

• a processor;
• a computer-readable data storage medium to store revenue over a plurality of time points, and a value of each of a plurality of drivers for each time point;
• and,
• a model generation component executable by the processor to:
• for each driver, perform cross-correlation to identify lag between the revenue and the driver;
• for each driver of one or more of the drivers, synchronize the revenue and the driver, based on the lag between the revenue and the driver;
• select one or more particular drivers from the one or more of the drivers, based on an analysis of the lags between the revenue and the one or more of the drivers as synchronized;
• and,
• construct a causal dynamic model for the revenue, using the particular drivers selected.