Abstract: A method for determining cognitive attributes from adjusted forecast to automatically recommend changes in adjusted forecast is provided. The method includes (i) receiving for a bias management service, action recommendation to mitigate or leverage from parameter forecast, which is a forecast of future values of a parameter associated with a factor group, tracking a modification in parameter forecast based on adjustment made by cognitive system in parameter forecast, classifying adjusted forecast as negative or positive cognitive system bias based on a deviation of the adjusted forecast from actual value to determine bias of the cognitive system, determining factors that associate with bias of the cognitive system, generating a skill score of cognitive system based on factor group of the bias management service using a machine learning engine and generating a recommendation to change a value of adjusted forecast based on cognitive attributes and skill score of cognitive system.

Abstract: A method for integrating a machine learning (ML) model that impacts different factor groups for generating a dynamic recommendation to collectively optimize a parameter is provided. The method includes (i) processing a specification information and operational data associated with a demand management service obtained from client devices (116A-N), (ii) training the ML models with processed specification information and the operational data to obtain a trained ML model that includes an anticipation ML model that optimizes demand parameter or recommendation ML model that generates recommendation for optimizing a factor group, (iii) integrating the trained ML model with the ML models by setting an output of a first ML model as a feature of a second ML model and (iv) determining a demand of a product using the trained ML models and quantifying probabilistic values that signify prediction of the demand.