Patent ID: 11922279
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method for selecting a transformed prediction model, associated with machine learning, for estimating the uncertainty of prediction relating to a business process without using training datasets, the method comprising:
determining, by a processor, a best-fit function corresponding to a prediction interval based on one or more benchmark datasets, wherein the one or more benchmark datasets is a combination of available open datasets related to a regression problem;
calculating, by the processor, one or more sets of prediction interval parameters associated with the best-fit function based on training a set of regression models with the one or more benchmark datasets, further comprises:
training the set of regression models based on a simulation approach;
extracting the one or more sets of prediction interval parameters from the set of regression models;
normalizing the one or more sets of prediction interval parameters;
splitting the one or more benchmark datasets into one or more subset benchmarks datasets; and
determining correlation between the one or more subset benchmarks dataset and the set of regression models;

creating, by the processor, one or more transformed predictions based on the one or more sets of parameters and based on a logical distance;
selecting, by the processor, a transformed prediction model based on a mean absolute correlation from the one or more transformed predictions; and
updating, by the processor, the best-fit function based on the identified one or more subset benchmark datasets and the normalized one or more sets of prediction interval parameters that are below a threshold;
determining, by the processor, the selected transformed prediction model, wherein the selected transformed prediction model is used to monitor and train correlation between the prediction interval and KPIs (Key Performance Indicator) of the business process; and
identify and removing, by the processor, correlations between the prediction interval and KPIs (Key Performance Indicator) that are under a threshold.