Abstract: Mechanisms are provided for automatic identification of a reconciliation computer tool for producing coherent reconciled data from base data generated by a computer model. A machine learning training operation is executed on one or more performance prediction computer model(s) (PPCMs) based on first input features of at least one hierarchical dataset, and second input features of a plurality of different reconciliation computer tools. The PPCM(s) generate a prediction of performance of a corresponding reconciliation computer tool based on the first and second input features. Features are extracted from a runtime hierarchical dataset and input into the trained PPCM(s) which generate predictions of performance of a plurality of reconciliation computer tools based on the extracted features of the runtime hierarchical dataset. The reconciliation computer tools are ranked relative to one another based on the predictions of performance.

Abstract: Mechanisms are provided for performing automated and dynamic reconciliation of forecasts for hierarchical datasets. A machine learning training is executed on a dynamic reconciliation computer model engine to train the dynamic reconciliation computer model engine, based on historical data and forecast data, to learn an association of reconciliation computer models with structural changes in a hierarchical dataset. Runtime forecast data is generated based on a runtime hierarchical dataset, and the trained dynamic reconciliation computer model engine is executed on the runtime forecast data to reconcile the runtime forecast data across a hierarchy of the runtime forecast data. The trained dynamic reconciliation computer model applies different reconciliation computer models to the runtime forecast data based on structural changes in the runtime forecast data.

Abstract: Mechanisms are provided for automatic identification of a reconciliation computer tool for producing coherent reconciled data from base data generated by a computer model. A machine learning training operation is executed on one or more performance prediction computer model(s) (PPCMs) based on first input features of at least one hierarchical dataset, and second input features of a plurality of different reconciliation computer tools. The PPCM(s) generate a prediction of performance of a corresponding reconciliation computer tool based on the first and second input features. Features are extracted from a runtime hierarchical dataset and input into the trained PPCM(s) which generate predictions of performance of a plurality of reconciliation computer tools based on the extracted features of the runtime hierarchical dataset. The reconciliation computer tools are ranked relative to one another based on the predictions of performance.

Abstract: Mechanisms are provided for performing automated and dynamic reconciliation of forecasts for hierarchical datasets. A machine learning training is executed on a dynamic reconciliation computer model engine to train the dynamic reconciliation computer model engine, based on historical data and forecast data, to learn an association of reconciliation computer models with structural changes in a hierarchical dataset. Runtime forecast data is generated based on a runtime hierarchical dataset, and the trained dynamic reconciliation computer model engine is executed on the runtime forecast data to reconcile the runtime forecast data across a hierarchy of the runtime forecast data. The trained dynamic reconciliation computer model applies different reconciliation computer models to the runtime forecast data based on structural changes in the runtime forecast data.