Method for dynamically recommending forecast adjustments that collectively optimize objective factor using automated ml systems

A system of dynamically generating recommendations to adjust an existing forecast that collectively optimizes an objective factor using an automated system of machine learning models, the system comprising:

• a probabilistic forecast management server that comprises:
• a memory that stores a set of instructions;
• and
• a processor that executes the set of instructions and is configured to:
• obtaining training data comprising external data and internal data associated with at least one factor group of a use-case, wherein the internal data includes historical data of a forecasted factor, historical data of an objective factor and historical and planned values of at least one factor of a factor group associated with the use-case;
• generating a probabilistic forecasting model for a forecasted factor of the use-case using the first automated system of machine learning models that generates different probabilistic forecasts of the forecasted factor with respect to different values of a factor;
• generating a relationship model based on a relationship between the forecasted factor, an objective factor and at least one factor using a second automated system of machine learning models, wherein the at least one factors is a constraint factor;
• determining an optimization model using a simulation based optimization, wherein the simulation is run on at least one adjustment of the existing forecast of the forecasted factor based on the probabilistic forecasting model and the relationship model using a third automated system of machine learning models, wherein said at least one adjustment includes an amount of return gained on said adjustment and an amount of risk undertaken in said adjustment;
• dynamically generating a recommendation to adjust an existing forecast that collectively optimizes the objective factor based on the optimization model using the third automated system of machine learning models;
• and
• dynamically applying the recommendation at the use-case to collectively optimize the objective factor.