Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: modeling for a subject process by machine learning with adaptive inputs. In one embodiment, the modeling may include: generating a model by use of machine learning with training data from measurements of successive components of a process to be modeled in order to predict measurements of a succeeding component within a statistically meaningful prediction range; adjusting the generated model by use of machine learning with less-deviation inducing measurements from a preceding component in case the measurement of the succeeding component is out of the prediction range; and presenting the adjusted model as a prediction model for the process.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: modeling for a subject process by machine learning with adaptive inputs. In one embodiment, the modeling may include: generating a model by use of machine learning with training data from measurements of successive components of a process to be modeled in order to predict measurements of a succeeding component within a statistically meaningful prediction range; adjusting the generated model by use of machine learning with less-deviation inducing measurements from a preceding component in case the measurement of the succeeding component is out of the prediction range; and presenting the adjusted model as a prediction model for the process.